{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "from pandas import DataFrame\n",
    "from sklearn import metrics\n",
    "from sklearn.decomposition import PCA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>面积</th>\n",
       "      <th>周长</th>\n",
       "      <th>暴雨</th>\n",
       "      <th>比降</th>\n",
       "      <th>断层</th>\n",
       "      <th>岩性</th>\n",
       "      <th>最大高程</th>\n",
       "      <th>最小高程</th>\n",
       "      <th>相对高差</th>\n",
       "      <th>主沟长度</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T001</th>\n",
       "      <td>65924920</td>\n",
       "      <td>37022</td>\n",
       "      <td>58</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1</td>\n",
       "      <td>4380</td>\n",
       "      <td>1557</td>\n",
       "      <td>2823</td>\n",
       "      <td>12031</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T002</th>\n",
       "      <td>30245122</td>\n",
       "      <td>26890</td>\n",
       "      <td>57</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1</td>\n",
       "      <td>4520</td>\n",
       "      <td>1557</td>\n",
       "      <td>2963</td>\n",
       "      <td>10973</td>\n",
       "      <td>270</td>\n",
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       "    <tr>\n",
       "      <th>T003</th>\n",
       "      <td>45788150</td>\n",
       "      <td>31986</td>\n",
       "      <td>57</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2</td>\n",
       "      <td>4640</td>\n",
       "      <td>1549</td>\n",
       "      <td>3091</td>\n",
       "      <td>11612</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T004</th>\n",
       "      <td>105370617</td>\n",
       "      <td>50908</td>\n",
       "      <td>72</td>\n",
       "      <td>0.78</td>\n",
       "      <td>3</td>\n",
       "      <td>5120</td>\n",
       "      <td>1620</td>\n",
       "      <td>3500</td>\n",
       "      <td>17338</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T005</th>\n",
       "      <td>10352295</td>\n",
       "      <td>14824</td>\n",
       "      <td>62</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1</td>\n",
       "      <td>4220</td>\n",
       "      <td>1561</td>\n",
       "      <td>2659</td>\n",
       "      <td>5731</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T196</th>\n",
       "      <td>29774013</td>\n",
       "      <td>28883</td>\n",
       "      <td>88</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0</td>\n",
       "      <td>2260</td>\n",
       "      <td>658</td>\n",
       "      <td>1602</td>\n",
       "      <td>10619</td>\n",
       "      <td>151</td>\n",
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       "    <tr>\n",
       "      <th>T197</th>\n",
       "      <td>35432134</td>\n",
       "      <td>27258</td>\n",
       "      <td>68</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1</td>\n",
       "      <td>2293</td>\n",
       "      <td>658</td>\n",
       "      <td>1635</td>\n",
       "      <td>9994</td>\n",
       "      <td>164</td>\n",
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       "    <tr>\n",
       "      <th>T198</th>\n",
       "      <td>16602244</td>\n",
       "      <td>27193</td>\n",
       "      <td>68</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0</td>\n",
       "      <td>2180</td>\n",
       "      <td>658</td>\n",
       "      <td>1522</td>\n",
       "      <td>10555</td>\n",
       "      <td>144</td>\n",
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       "    <tr>\n",
       "      <th>T199</th>\n",
       "      <td>13953579</td>\n",
       "      <td>19401</td>\n",
       "      <td>72</td>\n",
       "      <td>0.78</td>\n",
       "      <td>3</td>\n",
       "      <td>2368</td>\n",
       "      <td>659</td>\n",
       "      <td>1709</td>\n",
       "      <td>6507</td>\n",
       "      <td>263</td>\n",
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       "    <tr>\n",
       "      <th>T200</th>\n",
       "      <td>13371995</td>\n",
       "      <td>20438</td>\n",
       "      <td>72</td>\n",
       "      <td>0.84</td>\n",
       "      <td>3</td>\n",
       "      <td>2501</td>\n",
       "      <td>659</td>\n",
       "      <td>1842</td>\n",
       "      <td>8051</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             面积     周长  暴雨    比降  断层    岩性  最大高程  最小高程   相对高差  主沟长度\n",
       "ID                                                                 \n",
       "T001   65924920  37022  58  0.79   1  4380  1557  2823  12031   235\n",
       "T002   30245122  26890  57  0.79   1  4520  1557  2963  10973   270\n",
       "T003   45788150  31986  57  0.85   2  4640  1549  3091  11612   266\n",
       "T004  105370617  50908  72  0.78   3  5120  1620  3500  17338   202\n",
       "T005   10352295  14824  62  0.85   1  4220  1561  2659   5731   464\n",
       "...         ...    ...  ..   ...  ..   ...   ...   ...    ...   ...\n",
       "T196   29774013  28883  88  0.82   0  2260   658  1602  10619   151\n",
       "T197   35432134  27258  68  0.79   1  2293   658  1635   9994   164\n",
       "T198   16602244  27193  68  0.80   0  2180   658  1522  10555   144\n",
       "T199   13953579  19401  72  0.78   3  2368   659  1709   6507   263\n",
       "T200   13371995  20438  72  0.84   3  2501   659  1842   8051   229\n",
       "\n",
       "[200 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "excel_file=\"D:\\Lenovo\\Desktop\\云南大学\\空间数据挖掘\\实验数据\\实验一数据.xlsx\"\n",
    "#读取数据，设置第一行为索引，第一列为索引\n",
    "df=pd.DataFrame(pd.read_excel(excel_file,engine=\"openpyxl\",sheet_name=0,index_col=0))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_file=\"D:\\Lenovo\\Desktop\\云南大学\\空间数据挖掘\\实验数据\\实验一数据.csv\"\n",
    "df=pd.DataFrame(pd.read_csv(csv_file,encoding=\"gb2312\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>暴雨</th>\n",
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       "      <td>0.113904</td>\n",
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       "      <td>0.759453</td>\n",
       "      <td>0.230325</td>\n",
       "      <td>0.237569</td>\n",
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       "    <tr>\n",
       "      <th>T002</th>\n",
       "      <td>0.051860</td>\n",
       "      <td>0.189608</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.848866</td>\n",
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       "      <td>0.803940</td>\n",
       "      <td>0.207967</td>\n",
       "      <td>0.285912</td>\n",
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       "      <th>T003</th>\n",
       "      <td>0.078888</td>\n",
       "      <td>0.229796</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.879093</td>\n",
       "      <td>0.926195</td>\n",
       "      <td>0.844614</td>\n",
       "      <td>0.221471</td>\n",
       "      <td>0.280387</td>\n",
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       "      <th>T004</th>\n",
       "      <td>0.182496</td>\n",
       "      <td>0.379018</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.342477</td>\n",
       "      <td>0.191989</td>\n",
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       "    <tr>\n",
       "      <th>T005</th>\n",
       "      <td>0.017268</td>\n",
       "      <td>0.094453</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.773300</td>\n",
       "      <td>0.938669</td>\n",
       "      <td>0.707340</td>\n",
       "      <td>0.097189</td>\n",
       "      <td>0.553867</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>T196</th>\n",
       "      <td>0.051041</td>\n",
       "      <td>0.205325</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.060880</td>\n",
       "      <td>0.192510</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.287909</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.187278</td>\n",
       "      <td>0.139503</td>\n",
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       "    <tr>\n",
       "      <th>T198</th>\n",
       "      <td>0.028136</td>\n",
       "      <td>0.191997</td>\n",
       "      <td>0.354839</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.022519</td>\n",
       "      <td>0.138726</td>\n",
       "      <td>0.483871</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.375</td>\n",
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       "      <td>0.001040</td>\n",
       "      <td>0.447728</td>\n",
       "      <td>0.146217</td>\n",
       "      <td>0.229282</td>\n",
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       "<p>200 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "            面积        周长        暴雨    比降     断层        岩性      最大高程      最小高程  \\\n",
       "ID                                                                              \n",
       "T001  0.113904  0.269510  0.032258  0.56  0.125  0.813602  0.934511  0.759453   \n",
       "T002  0.051860  0.189608  0.000000  0.56  0.125  0.848866  0.934511  0.803940   \n",
       "T003  0.078888  0.229796  0.000000  0.80  0.250  0.879093  0.926195  0.844614   \n",
       "T004  0.182496  0.379018  0.483871  0.52  0.375  1.000000  1.000000  0.974579   \n",
       "T005  0.017268  0.094453  0.161290  0.80  0.125  0.773300  0.938669  0.707340   \n",
       "...        ...       ...       ...   ...    ...       ...       ...       ...   \n",
       "T196  0.051041  0.205325  1.000000  0.68  0.000  0.279597  0.000000  0.371465   \n",
       "T197  0.060880  0.192510  0.354839  0.56  0.125  0.287909  0.000000  0.381951   \n",
       "T198  0.028136  0.191997  0.354839  0.60  0.000  0.259446  0.000000  0.346044   \n",
       "T199  0.023530  0.130548  0.483871  0.52  0.375  0.306801  0.001040  0.405466   \n",
       "T200  0.022519  0.138726  0.483871  0.76  0.375  0.340302  0.001040  0.447728   \n",
       "\n",
       "          相对高差      主沟长度  \n",
       "ID                        \n",
       "T001  0.230325  0.237569  \n",
       "T002  0.207967  0.285912  \n",
       "T003  0.221471  0.280387  \n",
       "T004  0.342477  0.191989  \n",
       "T005  0.097189  0.553867  \n",
       "...        ...       ...  \n",
       "T196  0.200486  0.121547  \n",
       "T197  0.187278  0.139503  \n",
       "T198  0.199134  0.111878  \n",
       "T199  0.113588  0.276243  \n",
       "T200  0.146217  0.229282  \n",
       "\n",
       "[200 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据0-1标准化\n",
    "df_normalized = df.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))\n",
    "df_normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算皮尔逊相关系数\n",
    "df_corr = df_normalized.corr()\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "sns.heatmap(data=df_corr, annot=True, fmt='.2f', annot_kws={'size': 10}, cmap='Blues')\n",
    "# 设置颜色条\n",
    "cax = plt.gcf().axes[-1]\n",
    "cax.tick_params(labelsize=10)\n",
    "# 设置标题和坐标轴标签\n",
    "plt.title('皮尔逊相关系数矩阵', fontsize=18)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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RfsguqcThzGLc2yVQ7HKIiIjshlU9L/Hx8aioqDDfX7x4MdRqtfm+SqVCSEhIy1XnxLjPERERUeOsCi8pKSkwGAzm+++99x7KysrqHWMymVqkMGdX17R7+rIK5boakashIiKyH81a56WxoCIIQnNOSdeE+7oj3NcNBqMJx9n3QkREZGZVeBEEoV44+fN9alnc54iIiKghqxapM5lMGDRoEKRSKQBArVZj5MiRkMvlAICaGl7eaEkJUX5YfyIXRxheiIiIzKwKL19//bWt6qBG1O1zlJyrQmV1Ddzlt7UgMhERUZti1bfhk08+2ejjubm5KCsrQ9euXeHiwi/YlhLu64YwHzdcLqvC8UulGBzTTuySiIiIRGdVz0t+fj4eeughTJw40fzYvHnz0KFDB/Tp0wcxMTE4ffp0ixfprARBQP/I2llHnDJNRERUy6rw8uSTT8JoNGLBggUAgFWrVuHTTz/F7t27odVq8cQTT+DZZ5+1SaHOivscERER1WfVNZ7Dhw8jLS0NYWFhMBgMeOONNzBv3jzcfffdAICZM2fiww8/tEWdTquu7yUptwxV1Qa4yaUiV0RERCQuq0ZeOnfujF27dgGoXaCutLQUL7zwgvn5/fv3c4XdFtbezx3B3groDSaczOZ6L0RERFaNvHz66aeYMGECZsyYAYlEgu+++w5eXl7IzMzEtGnTcOjQIc5IamGCICAhyg8/ncrD4cxiDOwUIHZJREREorIqvCQkJCArKwtpaWmIiIhAu3a1s1/c3Nwwbtw4fPzxx4iLi7NJoc6sf5R/bXjJYtMuERGR1fOa3dzc0L17d7i6upofy8zMRKdOnRAcHNyixVGtuhlHp7LLoNUboJCx74WIiJyXVT0vqamp6NOnD0aNGgUAMBqNePTRRzFs2DA8//zziImJwW+//WaTQp1ZZIAHAr1cUW0w4mR2mdjlEBERicqq8PL0009jwIABWL16NQDgk08+wa+//opTp04hPT0dixYtwosvvmiTQp2ZIAjoH8V9joiIiAArw0tKSgpeffVVBAcHo6qqCosWLcIbb7yBLl26AADGjx+Pc+fO2aRQZ5dwbb2XI1kML0RE5NysCi99+vTBypUrUVBQgDlz5kAqlWLGjBnm59euXYvo6OgWL5Ku7zB98lrfCxERkbOyqmH3yy+/xEMPPYTXXnsNgYGB+PHHH+Hq6oq0tDSMHj0axcXF2LBhg61qdWrR7TwQ4OmKonIdknLKzJeRiIiInI1V4aVz585ITk5GSUkJfHx8IJHUDtwEBQVhyZIlGDhwIAIDA21SqLOr7Xvxw/+S83Ekq4ThhYiInJZFl412796NtLQ0830/Pz9zcKm7P2HCBAQGBkKv12PFihUtXykhIZL7HBEREVkUXjw9PfHAAw+YZxk1JSMjA/fddx8uX77cIsVRfXX7HJ3ILkV1jVHkaoiIiMRh0WWjfv36Yc+ePZg3bx4++OADPPbYY+jfvz+CgoJQXl6OjIwMbNq0CadOncLixYsxYsQIW9ftlDoFesLPXYaSSj2W7klH/yh/9Iv0g1QiiF0aERFRqxFMJpPJmhdkZmZiw4YNSE5ORmFhITw8PNChQwckJibivvvug4uL5W00s2fPxieffGK+Hx0djfT0dKSkpGDKlClIT0/HtGnTsHjxYgiCZV/QarUaSqUSKpUK3t7e1vxqdm97Sj6e/+8paG8YdQlRKrBgbCxGxHFDTCIiclzWfH9bHV5a0sCBA/Haa69h4MCBAACpVAq5XI6uXbsiMTERc+fOxezZs/HQQw9hypQpFp2zrYaX7Sn5mLXqBP78D6su0n0+uTcDDBEROSxrvr+tWuelJdXU1ODMmTMYMmQIfHx84OPjAy8vL2zbtg0qlQpLlixBdHQ0Fi5c6PQNwAajCW9tSW0QXACYH3trSyoMRtFyKBERUasRLbycPn0aRqMRvXr1gpubG0aMGIHs7GwkJSUhISEB7u7uAID4+HikpqY2eR6dTge1Wl3v1tYczSpBvkrb5PMmAPkqLY5y12kiInICooWX1NRUdOnSBd999x2Sk5Ph4uKC6dOnQ61WIzIy0nycIAiQSqUoLS1t9DyLFi2CUqk03yIiIlrrV2g1hZqmg8vtHEdEROTIrFqkDgB8fX0hl8shlUobfd5gMGDjxo3mPpamPP7443j88cfN95cuXYrIyEh069YNrq6u9Y5VKBSorKyEr69vg/PMnz+/3maQarW6zQWYQC9Fix5HRETkyKwOLxKJBEePHm3wuMlkwuDBg3Ho0CEEBwdbXUhgYCCMRiOCg4ORkpJS7zmNRgO5XN7o61xdXRuEnbamX6QfQpQKFKi0jfa9ALWzjvpdW8SOiIioLbP6spFUKkWHDh0a3Dp27AiZTIYOHTpYFCbmzp2LNWvWmO8fOnQIEokEPXr0wKFDh8yPZ2VlQafTwc/Peb+YpRIBC8bGArg+u+jPFoyN5XovRETkFKweeamursYvv/yCP8+wNplMqKysxC+//IL4+HgEBQXd9Dw9e/bEa6+9hqCgIBgMBjz33HP461//ivvvvx9qtRpff/01pkyZgoULF2LYsGFNXqZyFiPiQvD55N54a0tqo8277nKr/1ESERE5JKvXeRk2bBgUCgXkcnm9ACMIAkwmE/R6PV588UUMHTr0lueaP38+Pv/8c0ilUkyePBkLFy6Eh4cHNm/ejEmTJsHNzQ0SiQR79+5FbGysRfW11XVe6hiMJhzNKkGhRotALwV2nCnAyt8vooO/O3Y8PwQKmXOHPCIickw2WaTOZDIhPz8foaGh0Gq1eO6557Bs2TIAwNdff42Kigo8++yzza/+moKCAhw/fhwJCQnw97d8B+W2Hl7+rFxXg/v+vRdX1DrMvi8GLw7vLHZJREREVrPJInWzZs3Cp59+CgCQyWTYsWMHtFotHnroIbzzzjsIDw9vXtV/EhwcjNGjR1sVXJyRp6sLFoztDgD4Ym8GMq+Wi1wRERGRbVkcXu69916kpqYiJycHubm5AID8/Hy0a9cOmzZtQu/evZGdnY1Lly4hLS3NZgVTQyPjgnFPl3aoNhjx+qaUBv1IREREbYnFl40uXLiA2NhYxMTEwGQy4dy5c+bNEqOioiCTyWAymWA0GqHVanHp0iWbFt4UZ7tsVCe7uBLDP9gHXY0RHz3aC+N7hYldEhERkcVsctkoKioKrq6uSE1NRVpaGiIiIrB161YMHjwYMpkMn332GdLS0nDu3DnRgosza+/vjueGdgIA/PPnVKgq9SJXREREZBsWhxepVAofHx8UFhYCqJ1dlJiYiL179+LNN9/Eo48+ipdffhkGg8FmxdLN/W1IFKLbeaCovBr/b+dZscshIiKyCasWqduwYQPatWuHmpoa3HHHHebHH374YRw/fhw7d+7Ejh07WrxIsoyrixTvTOgBAFh9JBuncsrELYiIiMgGrF7n5WZqamrg4iLuYmnO2vNyoxfXnsKGE5fRPdQbm565Cy5S0fbfJCIisohNel4AwGg04ttvv0VNTU2jz4sdXKjWq6O6Qekmw5k8Nb49xP4jIiJqW6wKLxKJBFOmTLFVLdRCAjxdMW9EVwDAv3eeQ0Ej2wkQERE5qtvaVfrGEZYBAwYgJCQEwcHBCA8PR3h4OPr164euXbu2aKFknUf7RmDd8RyczC7D2z+fwdLH+4hdEhERUYuwuhmibm2XOn/88QeGDBkCpVKJCxcuYMWKFYiLi8PevXtbqka6DRKJgHcn9IBUImDr6QLsOVcodklEREQtwuqGXblcjurq6ibvA8D06dMhkUjwxRdftEyVVmDDbn3v/i8Vy/ZnIcLPDb+8cDc3biQiIrtk04ZdS8THxyMlJcWaU5ONPD+sM0KUCuSUVOHT3elil0NERNRsFoWXtLQ0JCYmYvHixRaddNSoUdi8eXOzCqOW4XHDxo3/+S0D6YUakSsiIiJqHovCy5IlSxAYGIhJkyZZdNKoqCj4+fk1qzBqOYndg3Bf10DoDSa89hM3biQiIsdm0WyjZcuWmf9uMpnQq1cvSKVSyGQyGAwGTJs2DQkJCZg4cSL8/f1tVizdHkEQ8Oa47jiYUYTDmSXYePIyHugdLnZZREREt8Xqhl2ZTIadO3dCr9dDrVajsLAQKSkp2LJlC0pKSvDyyy/jjTfeaDArqbWwYbdpn+/NwPvbz8LfQ45dc+6Gj7tc7JKIiIgAWPf93ezZRnVMJhO+/fZbzJ07FwMHDsT69eshlbb+zBaGl6ZV1xgx+uP9uFBYjkn92mPRAz3ELomIiAiADWcbAbX7FzWWdwRBwJNPPomTJ08iMzMTTz75pLWnJhuTu0jwzoQ4AMD3R7Nx/FKpyBURERFZz6rwotfrkZiYCJ1O1+QxYWFh+OWXXzB06NBmF0ctr3+UP/7Sp7bf5R8bT6PGYNn0dyIiInvRortK2wNeNrq1kopqDP33XpRV6vHa6G6YNjhK7JKIiMjJ2fSy0Z/V1NRg9uzZzT0NtSI/Dznmj6zde2rJL+eRV1YlckVERESWsyi8GI1G/PjjjwAAlUrV4PmvvvqqZasim/tLnwjc2cEXldUGvLXljNjlEBERWczi8DJlyhRcunQJ4eHhmDZtGgoKCgAALi4u9XaZJscgkQh4Z2IcXCQCdpy5gl1pV8QuiYiIyCIWhZe6gNKhQwds2rQJ2dnZ6Ny5M/7973/DYDCItqYLNU/XYG88PTgSAPDGpjOoqjaIXBEREdGtWdzzUhdQhg4dip07d2LlypXYvn07KioqbFYc2d7/3ReDMB83XC6rwse7L4hdDhER0S1ZHF5MJhM0Gg3UajXUajWGDRuG9evXm9d8qXuutLQU+fn5NiuYWpa73AVvjqvduHHZb5k4f4UbNxIRkX2zuFlFpVLBx8enyefrnjOZTBAEAQYDL0E4iuGxQRgeG4RfUq/gtY0p+GFGAi8FEhGR3bI4vHh7e+PSpUsNHjeZTOjYsaP5OYPBgKoqTr11NG+O644DF4pw9GIJfjyei7/cGSF2SURERI2y+LKRRCKBUqmEUqmEt7c31qxZg6VLl8LHxweCIJif8/PzQ1hYmC1rJhsI83HDC8NjAAALt6ahtKLh/lVERET2wOpF6s6ePYv+/ftj/vz5qKqqQk1NjS3qIhFMuSsSXYK8UFqpx3vbzopdDhERUaMsXufFYDBAr9dj3Lhx6NatGzIzM/H222/DxcWF/RFthEwqwbsTazdu/OFYDo5dLBG5IiIiooYsDi/Tp0+HTCbDqVOn8M0338DPz8/8HEdf2o47O/rh0b61/S7/2JgCPTduJCIiO2PxInX/7//9PwCAu7t7vecMBgPGjx/f8pWRaOaN6Ao/DznOXdHgqwNZYpdDRERUT7M3ZpTJZPjuu+9aohayE74ecrw6qhsA4MNfLyC3tFLkioiIiK6zKryYTCacOHHCfL+mpgYvvPBCg2O+//77lqmORPNg7zD0i/RDld6ANzenil0OERGRmVXhxWg0YuzYsTAYDHj55ZchlUqxceNGAMCxY8cA1G4jMHXq1JavlFqVIAh4d0Ltxo2/pl3BzjMFYpdEREQEwMrwIpVKIZfLzaMrgiBAIqk9xf33328+ztXVtWWrJFHEBHlh+pAoAMCbm8+gQsfGbCIiEp/F4WX+/Pk4e/YsBEGAi4sLZDJZvee9vb2vn1TS7FYashPPDY1BuK8b8lRafLyLGzcSEZH4LEoZBoMBGo0GQ4YMafIYk8mErKwsZGRktFhxJD43uRRvj6/duHH5gSycLVCLXBERETk7i8KLVCrFp59+ivPnzzd5jEajQc+ePdGrVy9uytjGDO0ahBHdg2EwmvCPjSkwGk1il0RERE7Mqus7Pj4+uHLlCp5//nlUV1ejsvL6FFqlUgm1Wg2NRgOpVNrihZK4FoyLhYdciuOXSrHueI7Y5RARkROzOLxUVFRgz549cHd3h1KpRGFhIcLDw1FZWYnKykqUl5fbsk4SWYjSDS8M7wwAWLTtLIrLdSJXREREzsri8DJo0CB88skn8PLywltvvYWwsDCcOnUKxcXF6Nu3Lzp16tSsQkaMGIGVK1cCAPbt24du3bohICAAS5YsadZ5qeU8NbAjuoV4o6xSj0XcuJGIiERicXj58ccfsWHDBvN9QRDQvn17hIeH4/3330dOTg4OHz4MAPUuJ1li9erV2LFjBwDg6tWrGDduHCZNmoRDhw5h9erV2LNnj1XnI9twubZxoyAAPx7Pxe/pRTiUUYxNpy7jUEYxDOyFISKiVuBi6YHR0dEAamceGQwGc0ARBAFjxozBqlWrMH78eOzatQtfffWVxQWUlJRgzpw56NKlC4DaIBMaGorXX38dgiDgjTfewIoVK3Dvvfda83uRjfRu74tJ/dpjzZFsPPHV0XqBJUSpwIKxsRgRFyJihURE1NZZ1bBb16QrlUqxYcMGmEwm847S99xzD55//nmMGzeu3oJ1tzJnzhxMnDgRCQkJAICkpCTce++9EAQBANCvXz8cP368ydfrdDqo1ep6N7KtPu19AaDBSEuBSotZq05ge0q+GGUREZGTsCq8yOVyXLlyBQAwcOBAGAwGTJgwwfz8vHnz0KdPH2g0GovOt2fPHuzatQuLFy82P6ZWqxEZGWm+7+3tjby8vCbPsWjRIiiVSvMtIiLCml+JrGQwmvCvnecafa4uyry1JZWXkIiIyGasXgr3xtVzXVxc8PHHH9d7bt26dfXCR1O0Wi1mzJiBzz//HF5eXvXOeeP2AgqF4qY9NPPnz4dKpTLfcnI4jdeWjmaVIF+lbfJ5E4B8lRZHs0parygiInIqt72Ov8FgwK5duwAAwcHB5sdTU1Px5JNP3vL1//znP9G3b1+MHj263uN+fn64evWq+b5Go4FcLm/yPK6urvD29q53I9sp1DQdXG7nOCIiImtZ3LD7/vvvQ6FQAAC8vLwwefJkzJ49G2fOnIHJdP0SwZo1a1BQcOsdiNesWYOrV6/Cx8cHQO0MpbVr1wKovSRV5+TJkwgLC7O0TLKxQC9Fix5HRERkLYtHXt5//33odDpotVp8+OGHkMvlcHGpzT7u7u4AgDNnzmDp0qV47733bnm+/fv3IyUlBadOncKpU6cwbtw4vP3228jOzsbBgwfx66+/Qq/XY/HixUhMTLzNX49aWr9IP4QoFRBuckyIUoF+kX6tVhMRETkXi0delEolXn75ZQC105kB1NsGYOvWrZgxYwbef/993HHHHbc8X3h4eL37np6eCAgIQEBAAD744AOMGjUKnp6e8PHxMS9eR+KTSgQsGBuLWatOQMD1Jt0bvTKyK6SSm8UbIiKi2yeYbrzmcxNRUVHIzMwEAMTHxyM5ORlKpRL33Xcftm3bhtDQUKxcuRKDBw9ukcKysrJw9uxZDB48GJ6enha/Tq1WQ6lUQqVSsf/Fhran5OOtLan1mnclAmA0AWPiQ/DJpDvM092JiIhuxZrvb4tHXhrLOH5+fkhMTMTu3buhVquxcuVK9O3b19wb0xyRkZEWzVoicYyIC8Hw2GAczSpBoUaLQC8FBAGYvPwIfk7OR3y4EtOHRItdJhERtUFWzTbS6/XQ6a5vyOfj44MZM2bA19cX6enpKCwsRGJionnhOmrbpBIBA6L9Mb5XGAZE+yMhyh8LxsYCAN7bdhYHLhSJXCEREbVFFoeX8vJyREZGIjo6Gn5+ftDpdKiurgZQu/KuUqnEhg0bYDAYsGjRIpsVTPZtckIH/KVPOIwm4NnvTyCnxLp9roiIiG7F4p6XP6upqcF///tfTJ48GXFxcdi2bRvy8vIgCAISExNRUFBQb7G51sKeF/Fp9QY8/J9DSM5VITbEG+tnDYSbXHrrFxIRkdOy5vvb4pGXK1eu4KmnnkJRURFMJhOqq6vx8MMPo7q6GidOnMCBAwcwfvx4yOVybN++XZTgQvZBIZPii8l9EOApR2q+Gq9sSG60Z4qIiOh2WBReCgsLce+990KtVsPT0xOpqanw9PSEm5ub+fb444+jsLAQgwcPRlBQkK3rJjsX6uOGzx7rDReJgE2n8rDiQJbYJRERURthUXjJzc3FxIkTsX79evNMosTERBgMBvPNaDSiuroaW7duRceOHW1ZMzmI/lH+eG10NwDAom1n8Xs6G3iJiKj5LAovvXv3xrvvvmtet8PLy6vBnkRA7aaKLbXOC7UNTw7siAd6h8FgNOHZ708it5QNvERE1Dy33bBrr9iwa3+0egMe+uJ3pFxWIy7MGz/OHAiFjA28RER0nU0Wqauzfv16c79LYyQSCXr27AkvLy9rT01tVF0D77hPDyLlshqvbjyNf/+lJ1fgJSKi22L1yItUKkX79u2bfF6tVqNXr17YtWtXs4u7HRx5sV+/ZxThiRVHYTCa8ObYWDx1F1dQJiKiWjYdefH29kZWVv2ZIzt27DDv/JyZmYlu3brBYDDU27iRaGB0AOaP7Ip3/peGf/4vDV1DvJEQ5S92WURE5GCs2h4AQIOh/vXr1+OBBx5ASkoKAMDf3x8vvfRSy1RHbc7TgyIxvlcoDEYTnll9AnllVWKXREREDsbq8HKjCxcu4G9/+xt++OEHxMXFAQCUSiXeffddjrpQowRBwHsPxKNbiDeKK6oxa9VxaPUGscsiIiIHYnF4+c9//oNt27bBYDAgPT0dV65cwcMPP4yFCxdizJgxtqyR2hg3uRRfPtEHPu4yJOWq8PpPKVyBl4iILGZRz0t1dTW++uoryGQyVFRU4O6770Z+fj68vb3Rp08fW9dIbVCEnzs+ndQbf/3qCNYdz0V8uBJPDOgodllEROQALBp5kcvlOHLkCA4cOAAvLy9cvnwZhYWFmDNnDoYNG4YPP/zQxmVSWzQoJgCvjOwKAHhrSyr+uFgickVEROQIbrthNyAgAK+//jq2b9+Ot99+G++++26LF0dt398GR2FMfAhqjCbMWnUCBSqt2CUREZGda1bDLgAMGDAAq1evhq+vb0vUQ05GEAQsfigeXYO9UFSuw8xVx6GrYQMvERE1zepF6tzc3DB//vxbHvfGG2/cdlHNwUXqHNOl4gqM+/QgVFV6TOoXgUUPxItdEhERtSKbLlI3atQopKWlwcWl8ZcajUaoVCprT0tOroO/Bz6edAee+voovj+agx5hPnisf9MrORMRkfNqsY0ZL1++jLCwMOh0OlRUVMDPz68lTms1jrw4tqV707F4+znIpAL+O30A+nTg5UgiImdgzfe3xT0v1dXViI+/PpQ/atQoAMCrr74KAOjfvz8AYN++fRg+fLjVRRMBwKy7ozGqRzD0BhNmrTqOQjUbeImIqD6Lw4tcLofJZMKyZcug0WiQnZ0NAPjhhx8A1K6sCwCLFy/G1KlTbVAqOQNBEPD/HuqJzkGeKNToMGv1CVTXGMUui4iI7IhVs43Onj2Ln3/+GXq9HnK5HADg5eUFoPZL5y9/+Qvat2+PZ555puUrJafh4eqC/zxxJ7wULjh+qRRv/3xG7JKIiMiOWBReqqur8eyzzyIqKgqbNm0yj7IAgEajQVBQEM6fPw8A+Oqrr2xTKTmVyAAPfPzoHRAEYNXhbPzwR7bYJRERkZ2wKLxotVoUFxfj6tWrSE5OxsiRI6FWq7F27VoYjUZcuXIFMTExMBqNeOSRR7hPDbWIe7sGYs7wzgCA1386g5PZpSJXRERE9sCi8OLt7Y3vv/8er732GsaMGYOnn34aMpkMa9asQceOHfHQQw9Br9dj/fr1qKmpwYIFC2xdNzmJv9/TCYndg1BtMGLWqhMo1LCBl4jI2Vnc81JTU4Ply5dj//79eOSRR3D06FFIpVKsW7cOc+fORUJCAgDgk08+waZNm6DX621WNDkPiUTAvx/uhU6BnihQa/EMG3iJiJyexeHFxcUFRUVFGD16NAoKCrB79250794dFRUVePfdd1FTU4Pp06fj008/xebNmyGTyWxZNzkRT1cX/OeJPvBydcEfF0vx7v9SxS6JiIhEZHF4+f333yGRSLBs2TLIZDKcP38eLi4uqKiogCAImDBhAgYMGIDDhw/j7bfftmXN5ISi23nig0d6AQC+OXQJ647liFsQERGJxqLtAbKzszFx4kR4eHigf//+eO2117Bu3ToEBwcjLy8PNTU16NSpE3r37g0AyMrKsmnR5JyGxQbh+WEx+PDXC/jHTynoEuyF+HAfscsiIqJWZtHIS/v27XHy5EnExMRg5syZWLhwIT788EP4+fkhLi4OXbp0wbPPPovo6GhIJBKOvJDNzB4ag2HdAlFdY8TM746jqFwndklERNTKLL5sFBoaiv/973/QaDQoKSmBXq9HXFwcnn32WSxZsgS///471q5di4ULF+Lvf/+7LWsmJyaRCFjySC9EBXggT1XbwKs3sIGXiMiZ3PbGjCUlJaioqEBERES9x1UqFYqLixEVFdUiBVqLGzM6h/RCDSZ89jvKdTWYelck/jG6G45mlaBQo0WglwL9Iv0glQhil0lERBay5vu7xXaVthcML85jx5kCzPjuOABA6SaDqur69PwQpQILxsZiRFyIWOUREZEVbLKrNJG9SewejFFxwQBQL7gAQIFKi1mrTmB7Sr4YpRERkQ0xvJDDMhhNOJFd1uhzdcOJb21JhcHYpgYXiYicHsMLOayjWSUoUDe9XYAJQL5Ki6NZJa1XFBER2RzDCzksS/c54n5IRERtC8MLOaxAL0WLHkdERI6B4YUcVr9IP4QoFbjVhOj1J3IaNPQSEZHjEj28lJWV4ciRIygtLRW7FHIwUomABWNjAaBBgLnx/o/HL+P+D/ZhV9qVVquNiIhsR9Twsm7dOnTs2BHTpk1DeHg41q1bBwBISUlB37594evri7lz56KNLUVDLWhEXAg+n9wbwcr6l4aClQp8Mbk31s0cgKgAD1xR6/D0N8fwwg+nUFpRLVK1RETUEkRbpE6lUqFTp07YtWsX4uPjsXLlSrz55ps4d+4cunbtisTERMydOxezZ8/GQw89hClTplh0Xi5S55wMRlOTK+xq9QZ88Mt5LNufCaMJCPCU45/j4zCyBxewIyKyFw6xwm5OTg5+++03PP744wCA5ORk3HXXXfjuu+8wdepU5Obmwt3dHUlJSXjmmWdw4MABi87L8EJNOZVThrnrknChsBwAMKpHMN4eH4cAT1eRKyMiIocILzfS6/WYPn06DAYDoqOjceTIEWzduhUAYDKZ4O/vj5KSxtfq0Ol00Omu7yysVqsRERHB8EKN0tUY8OnudCzdmwGD0QRfdxneHNcd43qGQhC4FxIRkVgcanuApKQkBAcHY/v27fj444+hVqsRGRlpfl4QBEil0iYbehctWgSlUmm+/XmjSKIbubpIMef+Ltj0zF3oFuKN0ko9/u+/p/C3b4/jyk0WvCMiIvsheniJj4/Hzp07ERMTg2nTpsHFxQWurvWH8RUKBSorKxt9/fz586FSqcy3nJyc1iibHFxcmBKbn70Lc4Z3hkwq4Ne0Kxi+ZB/WHcthgzgRkZ0TPbwIgoA+ffrgm2++wYYNG+Dn54erV6/WO0aj0UAulzf6eldXV3h7e9e7EVlCJpXgufti8PNzg9EzXAm1tgZzf0zGU1//gctlVWKXR0RETRAtvOzbtw9z584135fL5RAEAd26dcOhQ4fMj2dlZUGn08HPz0+MMskJdAn2wvpZA/HKyK6Qu0iw7/xVJH7wG1YfucRRGCIiOyRaeOncuTO+/PJLfPnll8jJycGrr76K+++/H6NGjYJarcbXX38NAFi4cCGGDRsGqVQqVqnkBFykEsy8Oxrb/m8w+nTwRbmuBv/YmILHlx9BdnHjlyyJiEgcos42+uWXX/D8888jJycHiYmJWLp0Kdq1a4fNmzdj0qRJcHNzg0Qiwd69exEbG2vROTlVmprLYDThm98vYvGOs9DqjXCTSTFvRBf8dUBHSCSckUREZAsON1W6MQUFBTh+/DgSEhLg7+9v8esYXqilXCquwLz1yTicWTtNv29HX7z/YDyi2nmKXBkRUdvTJsLL7WJ4oZZkNJqw5mg2Fm1NQ0W1Aa4uEsy5vzOeHhRlXsGXiIiaz6HWeSGyZxKJgMkJHbDjhSEYHBMAXY0RC7eexQOf/47zVzRil0dE5JQYXogsEO7rjm+n9sPiB+PhpXBBUk4Zxnx8AJ/uvgC9wSh2eUREToXhhchCgiDg4b4R+OWFu3Ff10BUG4z4187zmPDZQZzJUwGobfY9lFGMTacu41BGMQzGNnVVlojILrDnheg2mEwmbDqVhze3nEFZpR4uEgH3xwbhRHYZCm7YZiBEqcCCsbEYEccdrImIboYNuwwv1EoKNVq88dMZbD9T0OjzdS29n0/uzQBDRHQTbNglaiWBXgp89nhv+LjLGn2+7v8M3tqSyktIREQthOGFqJmOZpWgrFLf5PMmAPkqLY5mlbReUUREbRjDC1EzFWq0tz4IwNGLxTauhIjIOTC8EDVToJfCouM++OUCHlh6ENtO5/MSEhFRMzC8EDVTv0g/hCgVuNl6u24yKWQSASeyyzBr9Qnc+6+9WHkwCxW6mlark4iorWB4IWomqUTAgrG1G4f+OcAI124fPNITB+cPxbP3doKPuwzZJZV4c0sqBizahfe3n0WByrJLT0RExKnSRC1me0o+3tqSinzVzdd5qao24McTufjqQBayiioAADKpgLHxoZg2OAqxofz3loicD9d5YXghkRiMJhzNKkGhRotALwX6Rfo1uYGj0WjCr2lXsHx/Fo5evD4T6a5O/pg2OAr3dG4HQeDmj0TkHBheGF7IwSTllGHZ/kxsSykwN/PGBHri6UGRmHBHGBQyqcgVEhHZFsMLwws5qNzSSqw8eBH//SMH5deaeQM85XgioSOeGNABfh5ykSskIrINhheGF3Jwaq0ePxzNwdcHs5B3rYfG1UWCB/uE4+lBkYhu5ylyhURELYvhheGF2gi9wYhtKQVYvj8Tybkq8+PDugXi6UFRSIjyY18MEbUJDC8ML9TGmEy1jcDL9mdh19krqPuvNi7MG38bHIVRPUIgk9Zf+cCa5mEiIrExvDC8UBuWebUcKw5kYf2JXGj1RgBAqFKBp+7qiEf7tYe3QmbxtG0iInvB8MLwQk6gpKIaqw9fwjeHLqGoXAcA8JBL0T/KH7vPFjY4vm7M5fPJvRlgiMjuMLwwvJAT0eoN2HwqD8sPZOL8lfKbHisACFYqcGDeUF5CIiK7Ys33N7cHIHJwCpkUD/eNwI7nh2DeiC43PdYEIF+lxdGskpseR0RkzxheiNoIQRAQ6uNm0bHJuWW2LYaIyIZcxC6AiFpOoJfCouMWbTuLtcdyMCIuGCPjQtA91JtTronIYbDnhagNMRhNGPT+bhSotGjqP2xXFwkMRiNqjNcfC/d1w4juwRjZIxh3RPhCwn4YImplbNhleCEntj0lH7NWnQCAegHmxtlGAzsFYM/ZQmxPKcDec1dRpTeYjwv0ckVi92CMiAtG/0g/uEh5dZmIbI/hheGFnJw167xUVRuw7/xVbE/Jx660Qmiu7akEAD7uMgzvFoSRPYJxV6cAuLpwg0gisg2GF4YXottaYbe6xoiDGUXYkVKAnalXUFJRbX7O09UFQ7sGYkRcMO7p0g7ucrbMEVHLYXhheCFqthqDEUcvlmBHSgG2nynAFbXO/JyriwR3d26HkT2CMbRrEJRuspuei1sVENGtMLwwvBC1KKPRhFO5ZdieUoBtKfnIKakyPyeTChgYHYCRccEYHhsEf0/Xeq/lVgVEZAmGF4YXIpsxmUxIzVdje0oBtqcU4ELh9VV9JQLQL9IPI+NCkNg9GKdySjFr1YkGM5+4VQER/RnDC8MLUatJLyzHjjO1IzIpl9X1npNJBegNjX/EcKsCIrqRNd/f7LgjombpFOiJToGd8My9nZBTUokdZ2pHZI5dKm0yuAD1tyoYEO3fegUTkcPjAg5E1GIi/NwxbXAUfpw1EP8c392i1xRqtLc+iIjoBhx5ISKb6BToZdFxqw5fgquLBPd2DeQ6MkRkEYYXIrKJfpF+CFEqbrpVAQD8cbEUf1wshbfCBaPjQzChVxj6dvTjFgVE1CQ27BKRzdxqq4J/jO6Gq+U6bDqZhwL19ctHYT5uGNcrFBPvCEPnIMtGcIjIsXG2EcMLkd2wZJ0Xg9GEI1nF+OnkZWw7XVBvi4LYEG9MvCMM43qFIsjbsl2zicjxMLwwvBDZFWtW2NXqDdh9thAbT17G3nOF5hlLggDcFR2A8b1CMSIuGF6Km6/qS0SOheGF4YWoTSitqMb/Tufjp5OXcexSqflxVxcJhscGYeIdYRjSuR1k3PmayOExvDC8ELU5OSWV2HTqMjacvIzMqxXmx33dZRgTH4oJd4Shd3sfCAIbfYkcEcMLwwtRm2UymZByWY2NJy9jc1IeisqvbxjZ3s8dE+4Iw4ReoYhq59ngtdwgksh+MbwwvBA5hRqDEb9n1Db6bj9TgMpqg/m5nhE+mNArFGN7hiLA05UbRBLZOYcJL5s2bcILL7yA7OxsxMXF4fvvv0e3bt2QkpKCKVOmID09HdOmTcPixYstHgpmeCFyTpXVNfgl9Qp+OnkZv10ogsFY+9EmlQjoGuyFM3nqBq/hBpFE9sOa72/RutwyMjIwZcoUvPfee7h8+TI6d+6MadOmQafTYezYsejTpw+OHTuG1NRUrFy5UqwyichBuMtdML5XGL6e0g9HXr0Pb46NRc8IHxiMpkaDC3B97Zm3tqSaww4R2T/RRl5+/vln5OXlYfr06QCAPXv2YPTo0VizZg2mTp2K3NxcuLu7IykpCc888wwOHDjQ6Hl0Oh10uuvXvNVqNSIiIjjyQkQAgA0ncvHi2qRbHvf93/pjQHRAK1RERI1xiF2lx4wZU+/+uXPnEBMTg6SkJCQkJMDd3R0AEB8fj9TU1CbPs2jRIrz11ls2rZWIHJelDbnTvzuO4d2CMCgmAINiAhDoxQXxiOyVXTTsVldXo3v37njxxReRnp4OrVaLzz77zPx8u3btcP78efj6+jZ4LUdeiOhmDmUUY9Kyw1a/rmuwFwbHBGBQTDv06+gHNzk3jSSyJYcYebnRggUL4OHhgWnTpuG1116Dq6trvecVCgUqKysbDS+urq4NjiciqnOrDSIFAEFKBf71UDwOZhTjwIUipOSpcLZAg7MFGizbnwW5iwR9O/piUKd2GBwTgNgQb24cSSQi0cPL7t278dlnn+Hw4cOQyWTw8/NDSkpKvWM0Gg3kcrlIFRKRI5NKBCwYG4tZq05AQOMbRL45NhaDYtphUEw7zBsBlFRU42B6EfZfuIoDF4qQp9LiYHoxDqYX4/3tgJ+HHHd1CsDgmNpbiNJNhN+MyHmJetkoKysLCQkJ+Ne//oUnnngCQG2YmT59OtLT083HxMbGory8HFLprYdtOVWaiBpzu+u8mEwmZBZVYP/5qziQXoRDGcWouGE9GQCIbueBwTG1ozIJUf7wcL31/xdywTyi+hxinZeqqirceeeduOuuu7BkyRLz466urggLC8P777+PKVOm4G9/+xsKCgqwZcsWi87L8EJETWmJwKA3GHEyuwwHLlzFbxeKkJxbhhtnWcukAu5o74vBnQIwuHM79AhTNvgZXDCPqCGHCC+bNm3ChAkTGjyelZWF5ORkTJo0CW5ubpBIJNi7dy9iY2MtOi/DCxG1JlWlHr9nFGH/tctMOSVV9Z5XuslwVyd/c7/MmTwVZq060aD/hgvmkbNziPByKwUFBTh+/DgSEhLg7+9v8esYXohITJeKK7D/Qm2Q+T2jGBptTb3npRKhyQXxBADBSgUOzBvKS0jkdNpEeLldDC9EZC9qDEYkX1Zh//kiHEi/iuOXSmHJQr7f/y0BA6It/582oraA4YXhhYjs0A9/ZGPe+tO3PK5ToAfu6xqEnhE+iA9XIszHzeL93YgclcOt80JE5Aza+3lYdFx6YQXSCzPN9/095IgPVyI+3Ac9I2r/DPDk+lbkvBheiIhaiSUL5gV4uuLF+2Nw+rIaybllOJuvQXFFNfacu4o9566ajw3zcTMHmfhwJXqEKeGlkN1WXZy2TY6Gl42IiFrR9pR8zFp1AkDjC+b9ebaRVm9Aar4ayTllSM5VISm3DBlXKxqcVxCA6HaeiA9Xoue1QNMtxBsK2c3Xx+K0bbIX7HlheCEiO9bcwKDW6pGSq0JSrgrJubWh5nJZVYPjZFIBXYO9rweaCCViAr3Moyp1QYrTtskeMLwwvBCRnWvpSzVXNTok55bVCzQlFdUNjnOTSdEjTIm4MG9sOHEZZVX6Rs/HadvU2hheGF6IyMmZTCbkllYh6VqQScopQ8plVYOtDW6F07aptTC8MLwQETVgMJqQebUcSbkq/HTyMg6kF93yNZ2DPDEwOgDRgZ7o1M4TMUGe8PeQc+o2tThOlSYiogakEgExQV6ICfJCmI+bReHl/JVynL9SXu8xH3cZOrXzRKfA+rdQpRskzbjExFlPZCmGFyIiJ2TJtG0/Tznm3t8FmUUVSC8sx4VCDXJLq1BWqcexS6U4dqm03mvc5VJENxJqOvi5w0UquWk9nPVE1uBlIyIiJ2XttG0AqKo2ILOoHOmF9W8XiyugNzT+dSKTCujo74GYoNpLT9GBnogJ9EJUOw8oZFLOeiIA7HlheCEislBLjXjoDUZcKq5EemE5Mq6Wm0dqMgorUKVvvElYEIBwHzcUanTQ1RgbPwac9eQsGF4YXoiILGbLXhOj0YQ8VRUuFJYj44aRmguF5VA1MU27MVPu6ojBMQEI93VHuK8b3OW27Xpg/03rY3hheCEismsmkwlF5dX45veL+HRPutWv9/eQI9yvNshEXAs04b5uiPBzR5iP2y1XFr4Z9t+Ig+GF4YWIyCEcyijGpGWHb3ncnR18UVltQG5pJdTamlseH+jlag4z1wNO7d9Dfdwgd2m8gZj9N+LhVGkiInIIlsx6ClYq8MOMAebLNqoqPXJLK5FTUoXc0krkll7/M6ekEhXVBhRqdCjU6HAiu6zhOQUg2FtxfcTmWsAJVSrw+qYzjdZhulbLW1tSMTw2mJeQRMbwQkREopFKBCwYG4tZq05AQOOznhaMja0XFpRuMijdlOgeqmxwPpPJhLJKfW2QKa1sEHJySiuh1RuRr9IiX6XF0YuW12oCkK/SYuvpfCR2D25y9Kalsf+mIV42IiIi0bVWn4nJZEJxRTVySupGbOpCThXO5qtRqNFZfC5/DzkCvRUI8nZFsLfC/PcgLwWCrv3d39O1WUHDmfpv2PPC8EJE5HDEHmGwtP9GKgEMjc/sbkAiAO28XBHkrUCg17Vwcy3YBHorrgUdV/i6yxusTuxs/TfseSEiIocjlQiibgJpaf/N/pfvhVpbgytqLa6otShU62r/rtHiilqHQnXtn1fLdTAYTbii1uGKWgdA1eTPlkkFBHopEHht5KadlxwbT+ax/6YJHHkhIiK65nZWHW6KwWhCcYXOHG4K1DeGm2t/12hRVF592/U+0jcCA6L8EerjhlCf2stVsltsxXC7bD0yxstGDC9ERHSbWrvPpLrGiKJyXb1As//CVfySWmj1uSQCEOStuBZmagNNmI8bQpVuCLn2d6WbzOpdwVvjPWF4YXghIqJmcJT+myExAdAbalcxzi/TotqCZhx3udQcbsJ8FAhVut1w3w3BSkW9mVSt1XvDnhciIqJmcJT+m6+n9DOHKqPRhKIKHfLKtMgrq0JeWRUuX/uz7rHiimpUVhvM2zQ0RhCAdp6uCPVxQ4jSFb+dL7K73huGFyIiIjtzO+vfSCTXmn69FOgV4dPoebV6A/JV2j8Fm+vh5nJZFXQ1RvMif6dybl5n3do3R7NKWjXsMbwQERHZoRFxIfh8cu8GvSbBzeg1UcikiAzwQGSAR6PPm0wmlFRUI69Mi8tlVdh5pgAbTl6+5XkLNdpbHtOSGF6IiIjs1Ii4EAyPDW61/htBEODvWbu4Xo9wJZRuMovCS6CXwib1NIXhhYiIyI6J2X9jae9Nv0i/Vq2rdTZmICIiIodT13sDXO+1qdNU701rYHghIiKiJtX13gQr618aClYqRNuigJeNiIiI6KZau/fmVhheiIiI6JbEXvvmRrxsRERERA6F4YWIiIgcCsMLERERORSGFyIiInIoDC9ERETkUBheiIiIyKEwvBAREZFDYXghIiIih8LwQkRERA6lza2wazLV7nupVqtFroSIiIgsVfe9Xfc9fjNtLrxoNBoAQEREhMiVEBERkbU0Gg2USuVNjxFMlkQcB2I0GpGXlwcvLy8IgjgbRtmaWq1GREQEcnJy4O3tLXY5doPvS0N8Txrie9I4vi8N8T1pyJbviclkgkajQWhoKCSSm3e1tLmRF4lEgvDwcLHLaBXe3t78D6oRfF8a4nvSEN+TxvF9aYjvSUO2ek9uNeJShw27RERE5FAYXoiIiMihMLw4IFdXVyxYsACurq5il2JX+L40xPekIb4njeP70hDfk4bs5T1pcw27RERE1LZx5IWIiIgcCsMLERERORSGFyIiInIoDC8OZtOmTYiKioKLiwt69eqFtLQ0sUuyKyNGjMDKlSvFLsOuzJs3D2PHjhW7DLuwfPlyREREwN3dHffccw8yMzPFLonsSFFRESIjI3Hx4kXzY/zMbfx9uZEYn7sMLw4kIyMDU6ZMwXvvvYfLly+jc+fOmDZtmthl2Y3Vq1djx44dYpdhV5KTk7F06VJ89NFHYpciuoyMDLz99tvYtGkTzp49i+joaDz11FNilyWaxr6QUlJS0LdvX/j6+mLu3LkW7THTVhQVFWHMmDH13g9+5jb+vtxIrM9dhhcHkpaWhvfeew8PP/wwgoKCMGvWLJw8eVLssuxCSUkJ5syZgy5duohdit0wGo2YPn06XnjhBURFRYldjuhOnjyJhIQE9O7dG+3bt8fUqVORnp4udlmiaOwLSafTYezYsejTpw+OHTuG1NRUpxrFfPTRR/HYY4/Ve4yfuY2/L3XE/NxleHEgY8aMwfTp0833z507h5iYGBErsh9z5szBxIkTkZCQIHYpduOLL77A6dOn0bFjR2zevBnV1dVilySq2NhY7N69G6dOnYJKpcLSpUsxfPhwscsSRWNfSNu2bYNKpcKSJUsQHR2NhQsXYsWKFSJV2PqWLVuG2bNn13uMn7mNvy91xPzcZXhxUNXV1fj3v/+NmTNnil2K6Pbs2YNdu3Zh8eLFYpdiN8rLy7FgwQJERUXh0qVL+OCDDzBo0CBUVVWJXZpoYmNj8dBDD+GOO+6Aj48PDh06hH/9619ilyWKxr6QkpKSkJCQAHd3dwBAfHw8UlNTxShPFJGRkTd93lk/c5t6X8T+3GV4cVALFiyAh4eH011//TOtVosZM2bg888/h5eXl9jl2I0NGzagoqICe/bswVtvvYVffvkFGo0G3333ndiliebo0aPYsmULDh8+jLKyMkyaNAmjRo1yqr6OOo19IanV6nqPC4IAqVSK0tLS1izNbvEz9zp7+NxleHFAu3fvxmeffYY1a9ZAJpOJXY6o/vnPf6Jv374YPXq02KXYldzcXCQkJCAgIAAA4OLigvj4eKft8QCA77//Ho8++ij69+8PpVKJd955BxkZGUhKShK7NLvg4uLSYMl3hUKByspKkSqyH/zMrc8ePnddRPvJdFuysrIwadIkfPbZZ4iNjRW7HNGtWbMGV69ehY+PDwCgsrISa9euxdGjR7F06VJxixNReHh4g0tEly5dwsCBA0WqSHxGoxFFRUXm+xqNBpWVlTAYDCJWZT/8/PyQkpJS7zGNRgO5XC5SRfaBn7kN2cPnLsOLA6mqqsKYMWMwfvx4TJw4EeXl5QAADw8PCIIgcnXi2L9/P2pqasz3X3rpJSQkJDj1FFgAGD16NJ577jl88cUXGDNmDDZs2ICkpCSsW7dO7NJEM3jwYDz55JPo3bs3goKCsHz5cgQHByM+Pl7s0uxC3759sWzZMvP9rKws6HQ6+Pn5iViVuPiZ2zh7+NxleHEgO3fuRGpqKlJTUxt8yHTs2FG8wkQUHh5e776npycCAgLMl0uclb+/P7Zu3YqXXnoJL774IkJCQrB27VpERESIXZpoHnzwQaSlpeHDDz9Efn4+4uLisHHjRl4GuGbIkCFQq9X4+uuvMWXKFCxcuBDDhg2DVCoVuzTR8DO3cfbwuctdpYmInJQgCPW+iDdv3oxJkybBzc0NEokEe/fu5aUSsksML0REZFZQUIDjx48jISEB/v7+YpdD1CiGFyIiInIonCpNREREDoXhhYiIiBwKwwsRERE5FIYXIiIicigML0TUai5dutTgMZVKBZ1OJ0I1ROSoGF6IqFXk5eWhb9++OHjwIABAr9fDZDJh5syZePHFFwEAJpPJHGS+/PJLZGRk4Ntvv8WECROg1WrNx/3973/HihUrmvxZTz31FLp27Yp77rmn0ZsgCDAajTb+jYnIVhheiMjm9Ho9nnrqKbi7u2PixIlo164devTogSVLlmDbtm3YuXMnXFxc0KNHD/Tp0wdA7V47jzzyCKRSKeRyOX744QeUlZUBAPbt29dglc8bubq6wmg0oqamptGbXC6HRMKPPyJHxe0BiMjmnnjiCURERGDr1q04evQopk2bhv/9738YPHgw9u7di549e2LcuHEYPXo0Zs6cCZ1OB5PJhIkTJ+L06dMoKChAeno6unTpgqSkJKSnp2PIkCEAajdc1Ov19XZENhgMeOSRR5rc9XbQoEGt8nsTkW1wkToisrmioiIEBARg/vz5ePHFF6HX6xEaGor//ve/WLFiBbZt2waVSgWZTAZvb2+Ul5fjmWeegUqlwqZNmxAdHY377rsPer0efn5++OKLLxAQEACdTofS0lL89a9/xZdffmn+eRs3bkRSUtJNa3rjjTc4+kLkoBheiMimiouL8dtvv0Emk+H555/H9OnT0blzZwwdOhT9+vVDdXU1vL29AQDV1dVo3749tm/fjpycHEycOBEREREwGAyoqKjA6tWrMXnyZCQkJOCdd97Btm3b8Nlnn+Hnn38GALz33nv44YcfoFQq4enp2WRNBoMBpaWliIqKwpo1a1rlfSCilsPLRkRkUxUVFfjjjz+QkpKCoKAgVFZW4tixY/j222/RuXNnPPbYY3j00UfrvSYlJQWjR4/GRx99hOrqamzcuBGTJ0/GvHnzsHfvXrRr1w5AbRNwaGio+XWvvPIKXnnlFaxfvx45OTk3rWvq1Knm0EREjoUjL0TUKj755BOcPn0as2fPhqenJ9zd3fHwww8jKysLUqkUnp6e0Gg0eOSRR7Bw4UIUFxfD09MT3bp1w2+//Yb27dtj+fLlyMrKwpo1a5CVlYV//OMf8PX1xUsvvVTvZyUmJqJXr14YMGAAAOCll17C7Nmz0b59ewDApEmTcPHiRQQFBbX6+0BEzceRFyJqFZmZmcjJycGrr74Ko9GI1157DX5+ftizZw+CgoKQmZlpPvbgwYN48MEHodVqYTKZMGTIEKjVaiQkJGDr1q3YtWsXTp48iQMHDmDRokUNfpZEIsHGjRuxb98+ALU7Ja9YsQJubm4Aai9Psd+FyHExvBBRqzh8+DA++eQTZGRkYOPGjfj000+RnJyM7t27o6SkBN27d4fJZEJZWRmOHDmCgoICDB48GO+88w7uvvtujB8/HpMmTQJQu47LnDlzcO7cOdx5550NflbPnj0RFRUFX19fAEB2djYSEhLMl5v8/f1b7xcnohbH8EJENnf8+HEUFBSgT58+SE5OhqenJ5YvXw6TyYQHH3wQ3bp1AwCsW7fOPCJy+fJlyOVyPP/884iNjcXhw4fx/fffAwD++te/4sUXX8SMGTMgl8vr/aycnBzs3Lmz3uNqtRqHDx82j7zo9Xrs3bsXf/nLX1rj1yeiFsaeFyKyqbKyMvTr1w+vvPIKAOC7775D//798dxzz2H27NkQBAHr1q3D1KlTkZ2djZdffhl33303FAoFAGD9+vV45plnMHToUKSlpeHAgQP4+9//joyMDKSlpWHz5s246667zD9Pr9ejuLgYgYGB5iB05513YuXKlYiLiwMAVFZWQiqV1lsbhogcB0deiMimiouLMXToUEydOhWvv/46OnbsiBkzZmDkyJEYO3Ys3n77bQiCgK+++gqfffYZXnnlFfz000/YuXMnVq9eDYVCgd9//x1RUVH45ptv0LNnT4waNQr79u3D999/j/vvvx8LFy7E//3f/yEpKQlTpkyBj49PvZ6W/Px8zJw50xyIAKC8vBwjR47EggULxHhbiKgZOPJCRKLQ6XQ3Hfn44IMPMGjQIPTt29f82Nq1axEWFlZvpOXQoUMICAhATEyMTeslIvvB8EJEREQOhXMFiYiIyKEwvBAREZFDYXghIiIih8LwQkRERA6F4YWIiIgcCsMLERERORSGFyIiInIoDC9ERETkUP4/+9GmS2wvJ14AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 通过计算SSE绘制手肘图确定聚类数\n",
    "distortions = []  # 用来存放设置不同簇数时的SSE值\n",
    "for i in range(1,15):\n",
    "    kmModel = KMeans(n_clusters=i)\n",
    "    kmModel.fit(df_normalized)\n",
    "    distortions.append(kmModel.inertia_)  # 获取K-means算法的SSE\n",
    "# 绘制曲线\n",
    "plt.plot(range(1, 15), distortions, marker=\"o\")\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.xlabel(\"簇数量\")\n",
    "plt.ylabel(\"簇内误差平方和(SSE)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'KMeans' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m S \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m K:\n\u001b[1;32m----> 6\u001b[0m     kmeans \u001b[38;5;241m=\u001b[39m \u001b[43mKMeans\u001b[49m(n_clusters\u001b[38;5;241m=\u001b[39mk)\n\u001b[0;32m      7\u001b[0m     kmeans\u001b[38;5;241m.\u001b[39mfit(df_normalized)\n\u001b[0;32m      8\u001b[0m     labels \u001b[38;5;241m=\u001b[39m kmeans\u001b[38;5;241m.\u001b[39mlabels_\n",
      "\u001b[1;31mNameError\u001b[0m: name 'KMeans' is not defined"
     ]
    }
   ],
   "source": [
    "# 通过轮廓系数法确定聚类数\n",
    "K = range(2, 15)\n",
    "# 构建空列表，用于存储个中簇数下的轮廓系数\n",
    "S = []\n",
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(df_normalized)\n",
    "    labels = kmeans.labels_\n",
    "    # 调用模块metrics中的silhouette_score函数，计算轮廓系数\n",
    "    S.append(metrics.silhouette_score(df_normalized, labels, metric='euclidean'))\n",
    "\n",
    "# 设置绘图风格\n",
    "plt.style.use('ggplot')\n",
    "# 绘制K的个数与轮廓系数的关系\n",
    "plt.plot(K, S, 'b*-')\n",
    "plt.xlabel('簇的个数')\n",
    "plt.ylabel('轮廓系数')\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\ProgramData\\anaconda3\\envs\\myenv2\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1436: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# K-means聚类\n",
    "kms = KMeans(n_clusters=2, init='k-means++')\n",
    "data_fig = kms.fit(df_normalized)  # 模型拟合\n",
    "centers = kms.cluster_centers_  # 计算聚类中心\n",
    "labs = kms.labels_  # 为数据打标签\n",
    "df_labels = DataFrame(kms.labels_)  # 将标签存放为DataFrame\n",
    "\n",
    "# 将聚类结果为 0，1,2,3,4 的数据筛选出来 并打上标签\n",
    "df_A_0 = df_normalized[kms.labels_ == 0]\n",
    "df_A_1 = df_normalized[kms.labels_ == 1]\n",
    "# df_A_2 = df_normalized[kms.labels_ == 2]\n",
    "# df_A_3 = df_normalized[kms.labels_ == 3]\n",
    "\n",
    "df_A_0.insert(df_A_0.shape[1], '类别', 0)  # 打标签\n",
    "df_A_1.insert(df_A_1.shape[1], '类别', 1)\n",
    "# df_A_2.insert(df_A_2.shape[1], '类别', 2)\n",
    "# df_A_3.insert(df_A_3.shape[1], '类别', 3)\n",
    "df_labels_data = pd.concat([df_A_0, df_A_1])  # 数据融合\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_labels_data.to_excel('data_labeled.xlsx')  # 输出带有标签的数据\n",
    "\n",
    "# 输出最终聚类中心\n",
    "df_centers = DataFrame(centers)\n",
    "df_centers.to_excel('data_final_center.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            面积        周长        暴雨    比降     断层        岩性      最大高程      最小高程  \\\n",
      "ID                                                                              \n",
      "T001  0.113904  0.269510  0.032258  0.56  0.125  0.813602  0.934511  0.759453   \n",
      "T002  0.051860  0.189608  0.000000  0.56  0.125  0.848866  0.934511  0.803940   \n",
      "T003  0.078888  0.229796  0.000000  0.80  0.250  0.879093  0.926195  0.844614   \n",
      "T004  0.182496  0.379018  0.483871  0.52  0.375  1.000000  1.000000  0.974579   \n",
      "T005  0.017268  0.094453  0.161290  0.80  0.125  0.773300  0.938669  0.707340   \n",
      "...        ...       ...       ...   ...    ...       ...       ...       ...   \n",
      "T196  0.051041  0.205325  1.000000  0.68  0.000  0.279597  0.000000  0.371465   \n",
      "T197  0.060880  0.192510  0.354839  0.56  0.125  0.287909  0.000000  0.381951   \n",
      "T198  0.028136  0.191997  0.354839  0.60  0.000  0.259446  0.000000  0.346044   \n",
      "T199  0.023530  0.130548  0.483871  0.52  0.375  0.306801  0.001040  0.405466   \n",
      "T200  0.022519  0.138726  0.483871  0.76  0.375  0.340302  0.001040  0.447728   \n",
      "\n",
      "          相对高差      主沟长度  \n",
      "ID                        \n",
      "T001  0.230325  0.237569  \n",
      "T002  0.207967  0.285912  \n",
      "T003  0.221471  0.280387  \n",
      "T004  0.342477  0.191989  \n",
      "T005  0.097189  0.553867  \n",
      "...        ...       ...  \n",
      "T196  0.200486  0.121547  \n",
      "T197  0.187278  0.139503  \n",
      "T198  0.199134  0.111878  \n",
      "T199  0.113588  0.276243  \n",
      "T200  0.146217  0.229282  \n",
      "\n",
      "[200 rows x 10 columns]\n",
      "[[ 3.36339217e-01 -5.30824376e-01]\n",
      " [ 3.01245733e-01 -5.82149677e-01]\n",
      " [ 2.96910067e-01 -5.80431244e-01]\n",
      " [ 6.69082889e-01 -6.01993442e-01]\n",
      " [-6.58116358e-02 -5.95835726e-01]\n",
      " [ 2.00028768e-01 -5.89643209e-01]\n",
      " [-2.19873131e-01 -4.88959681e-01]\n",
      " [ 5.60288664e-01 -5.83210479e-01]\n",
      " [-1.09795359e-01 -4.88756287e-01]\n",
      " [ 9.01735079e-02 -4.95721370e-01]\n",
      " [ 4.91907478e-01 -4.55961774e-01]\n",
      " [ 3.02434764e-02 -4.03217314e-01]\n",
      " [-4.54000786e-01 -4.78875974e-01]\n",
      " [ 7.90507669e-04 -4.90563376e-01]\n",
      " [-3.19807789e-01 -3.92840954e-01]\n",
      " [ 6.72676111e-02 -4.82126554e-01]\n",
      " [ 5.87487514e-01 -4.47783282e-01]\n",
      " [-8.84869021e-02 -4.54371639e-01]\n",
      " [ 2.14539849e-01 -4.44119243e-01]\n",
      " [-3.10421712e-01 -5.73844420e-01]\n",
      " [ 3.04915010e-01 -3.93730891e-01]\n",
      " [-1.21185823e-01 -5.81987906e-01]\n",
      " [ 4.27855816e-01 -4.00587589e-01]\n",
      " [-3.13994954e-01 -3.08337652e-01]\n",
      " [-3.45854758e-01 -4.14352922e-01]\n",
      " [ 2.31638527e-01 -5.18781317e-01]\n",
      " [ 2.33748942e-01 -4.49789076e-01]\n",
      " [-5.02081114e-03 -3.86063238e-01]\n",
      " [-5.10323470e-01 -2.15029414e-01]\n",
      " [-2.08785687e-01 -2.94253019e-01]\n",
      " [-5.90442605e-01 -2.76054477e-01]\n",
      " [-6.36261859e-01 -2.24605390e-01]\n",
      " [ 1.13677099e-01 -3.42186626e-01]\n",
      " [ 1.15103482e-01 -3.27749650e-01]\n",
      " [-1.42967613e-01 -4.31131233e-01]\n",
      " [-2.35240715e-01 -3.47567558e-01]\n",
      " [ 7.50060703e-01 -2.13481544e-01]\n",
      " [-2.59689948e-01 -3.19789180e-01]\n",
      " [ 2.81817261e-01 -2.80517673e-01]\n",
      " [ 1.64881285e-01 -3.33410172e-01]\n",
      " [-1.29592127e-01 -3.40706927e-01]\n",
      " [ 3.08035071e-01 -2.77943966e-01]\n",
      " [-5.01801269e-01 -2.53246468e-01]\n",
      " [-3.49265277e-01 -2.72909769e-01]\n",
      " [ 7.04451795e-02 -2.84934094e-01]\n",
      " [ 4.98845696e-01 -2.24855637e-01]\n",
      " [-1.01615779e-01 -2.52732357e-01]\n",
      " [ 1.56661889e-01 -2.68356270e-01]\n",
      " [-7.46054600e-01 -8.03870775e-02]\n",
      " [-3.08915801e-01 -2.03234800e-01]\n",
      " [-6.30841894e-01 -1.21118572e-01]\n",
      " [-7.83707217e-01 -7.01322638e-02]\n",
      " [-8.27702473e-01 -7.63762609e-02]\n",
      " [ 1.54457813e-01 -2.03136125e-01]\n",
      " [-2.77603528e-01 -2.45346801e-01]\n",
      " [ 1.51881979e-01 -1.83058489e-01]\n",
      " [-1.08060797e-01 -2.16124639e-01]\n",
      " [-6.35036771e-02 -1.10356020e-01]\n",
      " [ 2.93580519e-01 -1.64529714e-01]\n",
      " [ 3.18239112e-01 -1.32086936e-01]\n",
      " [-6.69899050e-01 -5.91469633e-02]\n",
      " [-5.96280299e-01 -1.53083232e-01]\n",
      " [-4.16448195e-01 -6.91327708e-02]\n",
      " [ 2.57808021e-01 -1.78652975e-01]\n",
      " [-4.54198662e-01 -2.62852499e-01]\n",
      " [-6.13166561e-01 -1.25556870e-01]\n",
      " [-5.20556857e-01 -2.23694850e-01]\n",
      " [ 5.19617529e-01 -2.08527202e-01]\n",
      " [-4.80395788e-01 -8.02282713e-02]\n",
      " [ 4.14268284e-01 -1.49905883e-01]\n",
      " [-2.36773138e-01 -1.26677503e-01]\n",
      " [-1.76450107e-01 -2.12299693e-01]\n",
      " [-5.53220281e-01 -4.68235294e-02]\n",
      " [-1.31333936e-02 -2.33809308e-01]\n",
      " [-4.54122517e-01 -9.23401908e-02]\n",
      " [ 7.50853941e-01 -4.84478490e-02]\n",
      " [ 3.47875769e-01 -2.23035311e-01]\n",
      " [-6.37279692e-01 -1.31787303e-01]\n",
      " [-2.88791664e-01 -1.20294904e-01]\n",
      " [-3.61569302e-01 -1.59190274e-01]\n",
      " [-6.95721154e-01 -3.81412842e-02]\n",
      " [ 1.04012322e-02 -1.58572404e-01]\n",
      " [ 2.23773953e-01 -1.29547745e-01]\n",
      " [ 2.70836941e-01 -1.63615360e-01]\n",
      " [ 4.99117027e-01 -6.38703508e-02]\n",
      " [ 2.18880024e-01 -1.95150254e-01]\n",
      " [ 1.42500620e-01 -1.60043779e-01]\n",
      " [ 4.82966677e-01 -1.22624697e-01]\n",
      " [ 8.43975490e-02 -4.56402836e-02]\n",
      " [ 3.38847145e-01 -1.28308639e-01]\n",
      " [-8.91321181e-02 -1.51954908e-01]\n",
      " [ 1.49929874e-01 -1.69166355e-01]\n",
      " [-5.22341744e-01 -1.34539784e-01]\n",
      " [-1.82528714e-01  2.17640916e-02]\n",
      " [ 5.92991119e-01 -2.58437749e-02]\n",
      " [ 1.60324133e-01 -1.55010136e-01]\n",
      " [-1.45212524e-01 -1.41579240e-01]\n",
      " [ 1.88742459e-02  4.68849793e-02]\n",
      " [ 4.20192445e-01 -9.80539494e-03]\n",
      " [-1.52600421e-01 -9.09854048e-02]\n",
      " [ 1.63234208e-01 -1.04720888e-01]\n",
      " [ 3.08956959e-01 -3.04196341e-02]\n",
      " [-4.22018189e-01  4.03524271e-02]\n",
      " [ 3.70954222e-01 -1.22973586e-01]\n",
      " [-1.18448197e-01 -3.42878091e-02]\n",
      " [ 5.50023467e-01 -8.17490641e-02]\n",
      " [ 6.51669183e-01  4.01469657e-02]\n",
      " [-6.98853012e-01  4.56742625e-03]\n",
      " [-7.39806349e-02 -4.72482059e-02]\n",
      " [-2.67273244e-01  3.36104430e-04]\n",
      " [-4.96689629e-01  2.69569782e-02]\n",
      " [-7.80111536e-01  8.09385055e-02]\n",
      " [-9.83509341e-02  3.08845203e-02]\n",
      " [-6.42890358e-01  3.66505178e-02]\n",
      " [ 2.04878928e-01  3.85634190e-03]\n",
      " [ 6.02286699e-01  3.74398966e-02]\n",
      " [ 4.98677869e-01  3.76284021e-02]\n",
      " [ 4.10835131e-01 -5.99036119e-03]\n",
      " [ 5.02773629e-01  4.81130689e-02]\n",
      " [ 1.40832896e-01  3.19754877e-02]\n",
      " [-6.12241263e-01  9.84711129e-02]\n",
      " [-4.46164080e-01  1.15688894e-01]\n",
      " [ 4.35906400e-01  4.95943901e-02]\n",
      " [ 4.52118252e-01  3.43890601e-02]\n",
      " [ 2.22320348e-01  7.48391043e-02]\n",
      " [-5.29158551e-01  7.95814055e-02]\n",
      " [ 4.55086125e-01  6.25503104e-02]\n",
      " [-1.69417142e-01  1.13054369e-01]\n",
      " [-4.92292214e-01  1.08825688e-01]\n",
      " [-7.79178831e-01  1.64202827e-01]\n",
      " [-3.86109038e-01  2.90302683e-02]\n",
      " [-3.42992201e-01  1.13817028e-01]\n",
      " [ 5.00552831e-01  9.18120288e-02]\n",
      " [-5.59387520e-01  1.57336297e-01]\n",
      " [-3.99858260e-01  1.54828445e-01]\n",
      " [ 6.99740461e-01  1.36554753e-01]\n",
      " [ 2.24574064e-01  1.61726403e-01]\n",
      " [ 7.86321301e-01  1.60282106e-01]\n",
      " [-8.30886823e-01  1.84495218e-01]\n",
      " [-5.16686755e-01  2.58789062e-01]\n",
      " [-3.04782538e-01  2.02283481e-01]\n",
      " [-3.46143152e-01  2.28058017e-01]\n",
      " [ 6.71231078e-01  2.19060142e-01]\n",
      " [-3.23486334e-01  1.53014868e-01]\n",
      " [-3.70094643e-01  1.66549673e-01]\n",
      " [ 5.02422036e-01  1.61576163e-01]\n",
      " [ 3.75881816e-01  2.09928286e-01]\n",
      " [ 6.61379764e-01  2.07705646e-01]\n",
      " [ 2.02937875e-01  1.73020023e-01]\n",
      " [ 4.01635981e-01  1.77047999e-01]\n",
      " [-3.26612628e-01  3.10437411e-01]\n",
      " [ 1.45968924e+00  5.00776612e-01]\n",
      " [ 8.42600479e-01  2.46692987e-01]\n",
      " [ 4.72532536e-01  2.19064125e-01]\n",
      " [ 2.12759492e-01  3.22559116e-01]\n",
      " [ 5.26807563e-01  2.27572474e-01]\n",
      " [ 2.42019727e-01  3.42504409e-01]\n",
      " [ 4.79398946e-01  2.78816410e-01]\n",
      " [ 5.35606662e-02  3.48916092e-01]\n",
      " [-1.84029114e-01  4.09301977e-01]\n",
      " [ 3.12324139e-01  3.02555176e-01]\n",
      " [-4.77429116e-01  4.93260670e-01]\n",
      " [-5.20866552e-01  4.56205542e-01]\n",
      " [-2.02733715e-01  3.89934816e-01]\n",
      " [-1.48252702e-01  3.82183048e-01]\n",
      " [-2.55162772e-01  3.86322565e-01]\n",
      " [ 4.36519886e-01  2.77381636e-01]\n",
      " [-2.38905166e-01  4.38925420e-01]\n",
      " [-2.04132542e-01  3.54313936e-01]\n",
      " [ 3.31316435e-01  2.06462611e-01]\n",
      " [-4.01999504e-01  3.86456743e-01]\n",
      " [ 2.37662940e-01  4.58020895e-01]\n",
      " [ 7.80295833e-02  3.52713446e-01]\n",
      " [-5.15835667e-01  3.90388268e-01]\n",
      " [-2.10565998e-01  4.34162074e-01]\n",
      " [-2.69253107e-03  4.44607289e-01]\n",
      " [ 4.18890917e-01  3.01043680e-01]\n",
      " [ 4.73635610e-01  2.88091425e-01]\n",
      " [ 6.33764948e-02  4.80620487e-01]\n",
      " [ 3.97730735e-01  3.78915069e-01]\n",
      " [-2.58212445e-01  5.86437089e-01]\n",
      " [ 2.45657887e-01  2.13391586e-01]\n",
      " [-3.84164600e-01  5.12023449e-01]\n",
      " [ 1.30004433e+00  5.45447498e-01]\n",
      " [-4.81354271e-01  4.70641285e-01]\n",
      " [ 8.06903393e-01  5.84178072e-01]\n",
      " [ 3.57930660e-01  3.32951996e-01]\n",
      " [ 5.54342196e-02  5.80163750e-01]\n",
      " [-3.69599252e-01  5.79319762e-01]\n",
      " [-3.13625426e-01  7.37920344e-01]\n",
      " [-2.46575674e-01  5.60705083e-01]\n",
      " [-4.50732854e-01  5.27767744e-01]\n",
      " [-2.12431610e-01  6.09906173e-01]\n",
      " [-2.86122539e-02  6.20052477e-01]\n",
      " [ 3.79118554e-01  6.05984693e-01]\n",
      " [-3.36073701e-02  6.74076247e-01]\n",
      " [ 3.27593967e-02  5.93961308e-01]\n",
      " [-2.41022678e-02  6.09397860e-01]\n",
      " [ 2.18233072e-03  5.69350625e-01]\n",
      " [-1.65415894e-02  5.59994221e-01]]\n",
      "[0.47668908 0.27523148]\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=2)  \n",
    "data_pca = pca.fit_transform(df_normalized)     #把原数据传入方法，返回降维后的数据，等价于pca.fit(X) pca.transform(X)\n",
    "print(df_normalized)\n",
    "print(data_pca)\n",
    "print(pca.explained_variance_ratio_)    # 返回保留的n个成分各自的方差贡献比\n",
    "\n",
    "data_pca = pd.DataFrame(data_pca, columns=['PC1', 'PC2'])\n",
    "data_pca.insert(data_pca.shape[1], 'labels', labs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将多个类别使用PCA降维到X,Y两个方向\n",
    "\n",
    "# data_pca = pd.DataFrame(data_pca, columns=['PC1', 'PC2'])\n",
    "# data_pca.insert(data_pca.shape[1], 'labels', labs)\n",
    "\n",
    "# pca 对 K-means 的聚类中心降维，对应到散点图的二维坐标系中\n",
    "# pca = PCA(n_components=2)\n",
    "# pca.fit(centers)\n",
    "# data_pca_centers = pca.transform(centers)\n",
    "# data_pca_centers = pd.DataFrame(data_pca_centers, columns=['PC1', 'PC2'])\n",
    "\n",
    "# Visualize it:\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(data_pca.values[:, 0], data_pca.values[:, 1],c=data_pca.values[:,2],s=3, cmap='Accent')\n",
    "plt.scatter(df_centers.values[:, 0], df_centers.values[:, 1], marker='o', s=55, c='#8E00FF')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster\n",
      "-1    195\n",
      " 0      5\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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4OpRU5aGMKT8zMzNKqgghhJACJqulO7RQnRBCCCFEByipIoQQQgjRAUqqCCGEEEJ0gJIqQgghhBAdoKSKEEIIIUQHKKkihBBCCNEBSqoIIYQQQnSAkipCCCGEEB2gpIoQQgghRAeoonoBJo2Kw5nNHri40xMxkVIUsSsM9yGuaD2oOQyNDfI7PEIIIeSnwjHGWH4H8bOQSqUwNzdHbGzsN29TE/H+E8Y1nIXIkM9gwr9/hBzAgUPJyvZYeW0eTC1NdBA1IYQQ8nPT9vObpv8KqKW91+LTh6j/EioAYABjDMHPQrB+1I78C44QQgj5CVFSVQC9ffoeT24+hyATVJ4X5AKuH/JCdHhM3gZGCCGE/MQoqSqAnt95kWUbuUzAy/tBeRANIYQQQgBKqgokXqTdH5tITH+8hBBCSF6hT90CqEazyuA4TmMbiaE+nJzL5lFEhBBCCKGkqgCyKlEUDbs4qx2x4ngObYe1hLGZUR5HRgghhPy8KKkqoMZvGw6numUA/DcdyP873efsXhMDl/TMt9gIIYSQnxEV/yygjM2MsOr6fHiduofLf1/H57Bo2JQsCrf+zVCrRVXwPOXLhBBCSF6i4p95SJfFPwkhhBCSN6j4JyGEEEJIHqKkihBCCCFEByipIoQQQgjRAUqqCCGEEEJ0gJIqQgghhBAdoKSKEEIIIUQHKKkihBBCCNEBSqoIIYQQQnSAkipCCCGEEB2gbWp+IOHBkQjweQlexKNywwqwtDLP75AIIYSQnwYlVT+AmMhYrBq8Bd6nfYF/Nx0SiXm49mmMEWsHwNDYIH8DJIQQQn4ClFQVcIlxSRjfeA5CX31UJFQAIJcJ8Pj7Oj6+Cccyj9kQiUX5FyQhhBDyE6A1VQXchR2eCAn8AEEmZDonyAU8vv4Md8745UNkhBBCyM+FkqoC7tz2y2BfDlF9hRfxuLjzah5GRAghhPycKKkq4KI+RkNDTgVBLiAy5HPeBUQIIYT8pPI1qbpx4wbatWsHW1tbcByHEydOKM6lpaVhypQpqFKlCoyNjWFra4vffvsNHz58UOojKioKvXr1gpmZGSwsLDBw4EDEx8crtXn8+DEaNmwIAwMD2NvbY9myZZliOXz4MJycnGBgYIAqVarg3LlzSucZY5g9ezaKFSsGQ0NDuLq64uXLl7r7YeRQoWKWAKf+PC/iUbR44bwLiBBCCPlJ5WtSlZCQgGrVqmHDhg2ZziUmJuL+/fuYNWsW7t+/j2PHjiEwMBDt27dXaterVy88ffoUHh4eOHPmDG7cuIEhQ4YozkulUrRs2RIODg7w8/PD8uXLMXfuXGzdulXRxsvLCz169MDAgQPx4MEDdOzYER07doS/v7+izbJly7B27Vps3rwZPj4+MDY2hpubG5KTk3PhJ6O9NoNcwWnIqgS5ALf+TfMwIkIIIeTnxDHGNEwe5R2O43D8+HF07NhRbRtfX1/UrVsXwcHBKFGiBJ4/f46KFSvC19cXtWvXBgBcuHABbdq0QUhICGxtbbFp0ybMmDEDYWFh0NfXBwBMnToVJ06cQEBAAADg119/RUJCAs6cOaO4V7169VC9enVs3rwZjDHY2tpiwoQJmDhxIgAgNjYW1tbW2LVrF7p3767VM0qlUpibmyM2NhZmZmY5+TFlkhiXhJHO0xD66mOmxeo8z6Fywwr09h8hhBDyDbT9/C5Qa6piY2PBcRwsLCwAAN7e3rCwsFAkVADg6uoKnufh4+OjaNOoUSNFQgUAbm5uCAwMRHR0tKKNq6ur0r3c3Nzg7e0NAAgKCkJYWJhSG3Nzczg7OyvaqJKSkgKpVKr0pWtGpoZYdX0e6rnXUpoGFIl5tOjbBAvPTKOEihBCCMkDBaZOVXJyMqZMmYIePXoossSwsDBYWVkptROLxShUqBDCwsIUbRwdHZXaWFtbK85ZWloiLCxMcezLNl/28eV1qtqosmTJEsybNy+7j5ptFkXNMe/4ZKqoTgghhOSjApFUpaWloVu3bmCMYdOmTfkdjtamTZuG8ePHK76XSqWwt7fPtftZOxSFtUPRXOufEEIIIep990lVRkIVHBwMT09PpblMGxsbREREKLWXyWSIioqCjY2Nok14eLhSm4zvs2rz5fmMY8WKFVNqU716dbWxSyQSSCSS7DwuIYQQQgqo73pNVUZC9fLlS1y+fBmFCyuXBnBxcUFMTAz8/P6rGO7p6QlBEODs7Kxoc+PGDaSlpSnaeHh4oHz58rC0tFS0uXLlilLfHh4ecHFxAQA4OjrCxsZGqY1UKoWPj4+iDSGEEEJ+bvmaVMXHx+Phw4d4+PAhgPQF4Q8fPsS7d++QlpaGLl264N69e9i7dy/kcjnCwsIQFhaG1NRUAECFChXQqlUrDB48GHfv3sXt27cxcuRIdO/eHba2tgCAnj17Ql9fHwMHDsTTp09x8OBBrFmzRmlabsyYMbhw4QJWrlyJgIAAzJ07F/fu3cPIkSMBpL+ZOHbsWCxcuBCnTp3CkydP8Ntvv8HW1lbj24qEEEII+YmwfHT16lWG9HrgSl99+/ZlQUFBKs8BYFevXlX08fnzZ9ajRw9mYmLCzMzMWP/+/VlcXJzSfR49esQaNGjAJBIJs7OzY0uXLs0Uy6FDh1i5cuWYvr4+q1SpEjt79qzSeUEQ2KxZs5i1tTWTSCSsefPmLDAwMFvPGxsbywCw2NjYbF1HCCGEkPyj7ef3d1On6meQG3WqCCGEEJK7fsg6VYQQQggh3ytKqgghhBBCdICSKkIIIYQQHaCkihBCCCFEByipIoQQQgjRAUqqCCGEEEJ0gJIqQgghhBAdoKSKEEIIIUQHKKkihBBCCNEBSqoIIYQQQnSAkipCCCGEEB2gpIoQQgghRAcoqSKEEEII0QFKqgghhBBCdICSKkIIIYQQHaCkihBCCCFEByipIoQQQgjRAUqqCCGEEEJ0gJIqQgghhBAdoKSKEEIIIUQHKKkihBBCCNEBSqoIIYQQQnSAkipCCCGEEB2gpIoQQgghRAcoqSKEEEII0QFKqgghhBBCdICSKkIIIYQQHaCkihBCCCFEByipIoQQQgjRAUqqCCGEEEJ0gJIqQgghhBAdoKSKEEIIIUQHKKkihBBCCNEBSqoIIYQQQnSAkipCCCGEEB2gpIoQQgghRAcoqSKEEEII0QFKqgghhBBCdICSKkIIIYQQHaCkihBCCCFEByipIoQQQgjRAUqqCCGEEEJ0IF+Tqhs3bqBdu3awtbUFx3E4ceKE0nnGGGbPno1ixYrB0NAQrq6uePnypVKbqKgo9OrVC2ZmZrCwsMDAgQMRHx+v1Obx48do2LAhDAwMYG9vj2XLlmWK5fDhw3BycoKBgQGqVKmCc+fOZTsWQgghhPy88jWpSkhIQLVq1bBhwwaV55ctW4a1a9di8+bN8PHxgbGxMdzc3JCcnKxo06tXLzx9+hQeHh44c+YMbty4gSFDhijOS6VStGzZEg4ODvDz88Py5csxd+5cbN26VdHGy8sLPXr0wMCBA/HgwQN07NgRHTt2hL+/f7ZiIYQQQshPjH0nALDjx48rvhcEgdnY2LDly5crjsXExDCJRML279/PGGPs2bNnDADz9fVVtDl//jzjOI6FhoYyxhjbuHEjs7S0ZCkpKYo2U6ZMYeXLl1d8361bN+bu7q4Uj7OzMxs6dKjWsWgjNjaWAWCxsbFaX0MIIYSQ/KXt5/d3u6YqKCgIYWFhcHV1VRwzNzeHs7MzvL29AQDe3t6wsLBA7dq1FW1cXV3B8zx8fHwUbRo1agR9fX1FGzc3NwQGBiI6OlrR5sv7ZLTJuI82saiSkpICqVSq9EUIIYSQH9N3m1SFhYUBAKytrZWOW1tbK86FhYXByspK6bxYLEahQoWU2qjq48t7qGvz5fmsYlFlyZIlMDc3V3zZ29tn8dSEEEIIKai+26TqRzBt2jTExsYqvt6/f5/fIRFCCCEkl3y3SZWNjQ0AIDw8XOl4eHi44pyNjQ0iIiKUzstkMkRFRSm1UdXHl/dQ1+bL81nFoopEIoGZmZnSFyGEEEJ+TN9tUuXo6AgbGxtcuXJFcUwqlcLHxwcuLi4AABcXF8TExMDPz0/RxtPTE4IgwNnZWdHmxo0bSEtLU7Tx8PBA+fLlYWlpqWjz5X0y2mTcR5tYCCGEEPKTy6OF8yrFxcWxBw8esAcPHjAAbNWqVezBgwcsODiYMcbY0qVLmYWFBTt58iR7/Pgx69ChA3N0dGRJSUmKPlq1asVq1KjBfHx82K1bt1jZsmVZjx49FOdjYmKYtbU169OnD/P392cHDhxgRkZGbMuWLYo2t2/fZmKxmK1YsYI9f/6czZkzh+np6bEnT54o2mgTS1bo7T9CCCGk4NH28ztfk6qrV68yAJm++vbtyxhLL2Uwa9YsZm1tzSQSCWvevDkLDAxU6uPz58+sR48ezMTEhJmZmbH+/fuzuLg4pTaPHj1iDRo0YBKJhNnZ2bGlS5dmiuXQoUOsXLlyTF9fn1WqVImdPXtW6bw2sWSFkipCCCGk4NH285tjjLH8GiX72UilUpibmyM2NpbWVxFCCCEFhLaf39/tmipCCCGEkIKEkipCCCGEEB2gpIoQQgghRAcoqSKEEEII0QFKqgghhBBCdICSKkIIIYQQHaCkihBCCCFEB8T5HQDRjfiYBFzecwNPvQLAi3jUaF4VTbv/AomhJL9DI4QQQn4KVPwzD+VW8U8/j0eY22k5UhJTwXEAOA6CXIBFUTMsuTATZWo46uxehBBCyM+Gin/+JEJefsSs9kuRkpQKxhgEgUGQCwAAaVQ8JreYD2lUXD5HSQghhPz4KKkq4E6uOw9BLoAJmQccBbmA+OgEXNp1Le8DI4QQQn4ylFQVcLeO+0AuE9SeZ4zh9om7eRgRIYQQ8nOipKqAS01Oy7pNUmoeREIIIYT83CipKuDK1nQEL1L/xygS8yhXu3QeRkQIIYT8nCipKuA6jGytWJiuilwmoO2wlnkYESGEEPJzoqSqgKvXthbch7YAAHA8pzieMXo1cHFPlK5WMj9CI4QQQn4qVPyzgOM4DmM2Dkbl+k44tvoMXt4PAscBlRs4oeuE9qjXtlZ+h0gIIYT8FKj4Zx7KreKfX5KlycDxHEQiUa70TwghhPxstP38ppGqH4xYj/5ICSGEkPxAa6oIIYQQQnSAkipCCCGEEB2gpIoQQgghRAcoqSKEEEII0QFKqgghhBBCdICSKkIIIYQQHaCkihBCCCFEByipIoQQQgjRAUqqCCGEEEJ0gJIqQgghhBAdoKSKEEIIIUQHKKkihBBCCNEBSqoIIYQQQnSAkipCCCGEEB2gpIoQQgghRAcoqSKEEEII0QFKqgghhBBCdICSKkIIIYQQHaCkihBCCCFEByipIoQQQgjRAUqqCCGEEEJ0gJIqQgghhBAdoKSKEEIIIUQHvuukSi6XY9asWXB0dIShoSFKly6NBQsWgDGmaMMYw+zZs1GsWDEYGhrC1dUVL1++VOonKioKvXr1gpmZGSwsLDBw4EDEx8crtXn8+DEaNmwIAwMD2NvbY9myZZniOXz4MJycnGBgYIAqVarg3LlzufPghBBCCClwvuuk6o8//sCmTZuwfv16PH/+HH/88QeWLVuGdevWKdosW7YMa9euxebNm+Hj4wNjY2O4ubkhOTlZ0aZXr154+vQpPDw8cObMGdy4cQNDhgxRnJdKpWjZsiUcHBzg5+eH5cuXY+7cudi6dauijZeXF3r06IGBAwfiwYMH6NixIzp27Ah/f/+8+WEQQggh5PvGvmPu7u5swIABSsc6derEevXqxRhjTBAEZmNjw5YvX644HxMTwyQSCdu/fz9jjLFnz54xAMzX11fR5vz584zjOBYaGsoYY2zjxo3M0tKSpaSkKNpMmTKFlS9fXvF9t27dmLu7u1Iszs7ObOjQoVo/T2xsLAPAYmNjtb6GEEIIIflL28/v73qk6pdffsGVK1fw4sULAMCjR49w69YttG7dGgAQFBSEsLAwuLq6Kq4xNzeHs7MzvL29AQDe3t6wsLBA7dq1FW1cXV3B8zx8fHwUbRo1agR9fX1FGzc3NwQGBiI6OlrR5sv7ZLTJuI8qKSkpkEqlSl+EEEII+TGJ8zsATaZOnQqpVAonJyeIRCLI5XIsWrQIvXr1AgCEhYUBAKytrZWus7a2VpwLCwuDlZWV0nmxWIxChQoptXF0dMzUR8Y5S0tLhIWFabyPKkuWLMG8efOy+9iEEEIIKYC+65GqQ4cOYe/evdi3bx/u37+P3bt3Y8WKFdi9e3d+h6aVadOmITY2VvH1/v37/A6JEEIIIbnkux6pmjRpEqZOnYru3bsDAKpUqYLg4GAsWbIEffv2hY2NDQAgPDwcxYoVU1wXHh6O6tWrAwBsbGwQERGh1K9MJkNUVJTiehsbG4SHhyu1yfg+qzYZ51WRSCSQSCTZfWxCCCGEFEDf9UhVYmIieF45RJFIBEEQAACOjo6wsbHBlStXFOelUil8fHzg4uICAHBxcUFMTAz8/PwUbTw9PSEIApydnRVtbty4gbS0NEUbDw8PlC9fHpaWloo2X94no03GfQghhBDyk8ujhfM50rdvX2ZnZ8fOnDnDgoKC2LFjx1iRIkXY5MmTFW2WLl3KLCws2MmTJ9njx49Zhw4dmKOjI0tKSlK0adWqFatRowbz8fFht27dYmXLlmU9evRQnI+JiWHW1tasT58+zN/fnx04cIAZGRmxLVu2KNrcvn2bicVitmLFCvb8+XM2Z84cpqenx548eaL189Dbf4QQQkjBo+3n93edVEmlUjZmzBhWokQJZmBgwEqVKsVmzJihVPpAEAQ2a9YsZm1tzSQSCWvevDkLDAxU6ufz58+sR48ezMTEhJmZmbH+/fuzuLg4pTaPHj1iDRo0YBKJhNnZ2bGlS5dmiufQoUOsXLlyTF9fn1WqVImdPXs2W89DSRUhhBBS8Gj7+c0x9kV5cpKrpFIpzM3NERsbCzMzs/wOhxBCCCFa0Pbz+7teU0UIIYQQUlBQUkUIIYQQogOUVBFCCCGE6AAlVYQQQgghOpCtpCotLQ2TJ09GmTJlULduXfz1119K58PDwyESiXQaICGEEEJIQZCtpGrRokX4+++/MWzYMLRs2RLjx4/H0KFDldrQy4SEEEII+Rlla5uavXv3Yvv27Wjbti0AoF+/fmjdujX69++vGLXiOE73URJCCCGEfOeyNVIVGhqKypUrK74vU6YMrl27Bi8vL/Tp0wdyuVznARJCCCGEFATZSqpsbGzw+vVrpWN2dna4evUqfH190a9fP13GRgghhBBSYGQrqWrWrBn27duX6bitrS08PT0RFBSks8AIIYQQQgqSbK2pmjVrFgICAlSes7Ozw/Xr1+Hh4aGTwAghhBBCChLa+y8P0d5/hBBCSMGT63v/7dmzB/Xr14etrS2Cg4MBAKtXr8bJkydz2iUhhBBCSIGVo6Rq06ZNGD9+PNq0aYOYmBjFW38WFhZYvXq1LuMjhBBCCCkQcpRUrVu3Dtu2bcOMGTOUKqjXrl0bT5480VlwhBBCCCEFRY6SqqCgINSoUSPTcYlEgoSEhG8OihBCCCGkoMlRUuXo6IiHDx9mOn7hwgVUqFDhW2MihBBCCClwslVSIcP48eMxYsQIJCcngzGGu3fvYv/+/ViyZAm2b9+u6xgJ0eh9YCjC3kbCvIgpytYsRVslEUIIyRc5SqoGDRoEQ0NDzJw5E4mJiejZsydsbW2xZs0adO/eXdcxEqJSoO8rbBj9F577vFQcsy1tjUF/9EHDTs75GBkhhJCfUbbrVMlkMuzbtw9ubm6wtrZGYmIi4uPjYWVllVsx/jCoTpXuBN57jXGNZkGeKoMgfPErzAFgwNQ9o9G8V8N8i48QQsiPI9fqVInFYgwbNgzJyckAACMjI0qoSJ7bNG4n5Gly5YQKAP79dv2oHUhNScv7wAghhPy0crRQvW7dunjw4IGuYyFEKx9eh+Hp7UAIckFtm/iYBNw5fS8PoyKEEPKzy9Gaqt9//x0TJkxASEgIatWqBWNjY6XzVatW1Ulw5PsnS5Phzhk/vHoQBD2JHpzda6JMdcdcvWfk+89ZtuFFPCLefcrVOAghhJAv5SipyliMPnr0aMUxjuPAGAPHcYoK6+TH5n87AAu6rkRUWAxEeiIwgWHXrAOo4VoFMw+Mg1kh01y5r3nRrNejCXJBq3aEEEKIruQoqQoKCtJ1HKSAeRcQiiktFiAtNX3dkjztv0T60dWnmNFmMdZ4LQLP53h7SbUcKhaHQyV7vHsWAnXvWUgM9fFLhzo6vzchhBCiTo6SKgcHB13HQQqYQ8tPQi6TgX29UBzpo0QBd1/Bz+Mx6rhV1/m9OY7DkGV9MLPtEsXbfl/rPbsrjM2MdH5vQgghRJ0cJVV///23xvO//fZbjoIhBQNjDFcP3IZcpn6huEjM4/pBr1xJqgCgbusamH1kAtYM34aYiFjF9LOBsQR9ZndF14ntc+W+hBBCiDo5SqrGjBmj9H1aWhoSExOhr68PIyMjSqp+cIJcQGpSapZt4mNzdx/IBv9zRr22teB36ZGiorqze00Ymhjm6n0JIYQQVXKUVEVHR2c69vLlSwwfPhyTJk365qDI900kFqFo8cKIDFH/Fh7H87ArUyzXYxHrieHsXivX70MIIYRkRWeriMuWLYulS5dmGsUiP6Z2w93A8er32BPkAloPap6HERFCCCH5S6evZonFYnz48EGXXZLvVMfRrVGqqgN4kepfIWuHoljaZy3WjtiOoCfBeRwdIYQQkveyvfcfAJw6dUrpe8YYPn78iPXr18Pe3h7nz5/XWYA/kh9t778EaSJ2zz6I8zuuIDkhBQDA81z61jH/vpUnEvOQywQMXNIL3ad0zNd4CSGEkJzQ9vM7R0nV17WHOI5D0aJF0axZM6xcuRLFiuX+WpqC6EdLqjIkJ6bg3fMQTG+zGHFR8Wq3j5l/cgpc2tXO4+gIIYSQb6Pt53eOFqoLgvpX6cnPx8BIgnfPQxEbKVXbhhfxOLziFCVVhBBCflg5WlM1f/58JCYmZjqelJSE+fPnf3NQpOC5f/mx2vVVQPrC9Sc3nysqsBNCCCE/mhwlVfPmzUN8fHym44mJiZg3b943B0UKHnVTfl9TVYGdEEII+RHkKKnK2Dj5a48ePUKhQoW+OShS8Dg5l9WYMHE8B8cqJaBvoJ+HURFCCCF5J1trqiwtLcFxHDiOQ7ly5ZQSK7lcjvj4eAwbNkznQZLvX4s+jbBj2l6kJKWqTK6YwNBpjHs+REYIIYTkjWwlVatXrwZjDAMGDMC8efNgbm6uOKevr4+SJUvCxcVF50GS75+xuTHmHpuEWe2XQi4XIPy7LyAv4iHIBbj1bwq3/k3zOUpCCCEk9+SopML169fxyy+/QE9PLzdi+mH9qCUVvhTy4gOOrz2HW8d8kJKcijI1HNFhRGs0+F9dlVPGhBBCyPcuV+tUfSk5ORmpqcqb6/6oCcO3+hmSKkIIIeRHo+3nd44WqicmJmLkyJGwsrKCsbExLC0tlb4IIYQQQn42OUqqJk2aBE9PT2zatAkSiQTbt2/HvHnzYGtri7///lunAYaGhqJ3794oXLgwDA0NUaVKFdy7d09xnjGG2bNno1ixYjA0NISrqytevnyp1EdUVBR69eoFMzMzWFhYYODAgZlKQjx+/BgNGzaEgYEB7O3tsWzZskyxHD58GE5OTjAwMECVKlVw7tw5nT4rIYQQQgquHCVVp0+fxsaNG9G5c2eIxWI0bNgQM2fOxOLFi7F3716dBRcdHY369etDT08P58+fx7Nnz7By5Uql0bBly5Zh7dq12Lx5M3x8fGBsbAw3NzckJycr2vTq1QtPnz6Fh4cHzpw5gxs3bmDIkCGK81KpFC1btoSDgwP8/PywfPlyzJ07F1u3blW08fLyQo8ePTBw4EA8ePAAHTt2RMeOHeHv76+z5yWEEEJIAcZywNjYmAUHBzPGGLOzs2M+Pj6MMcbevHnDjI2Nc9KlSlOmTGENGjRQe14QBGZjY8OWL1+uOBYTE8MkEgnbv38/Y4yxZ8+eMQDM19dX0eb8+fOM4zgWGhrKGGNs48aNzNLSkqWkpCjdu3z58orvu3Xrxtzd3ZXu7+zszIYOHar188TGxjIALDY2VutrCCGEEJK/tP38ztFIValSpRAUFAQAcHJywqFDhwCkj2BZWFjoJtsDcOrUKdSuXRtdu3aFlZUVatSogW3btinOBwUFISwsDK6uropj5ubmcHZ2hre3NwDA29sbFhYWqF37vz3nXF1dwfM8fHx8FG0aNWoEff3/ClO6ubkhMDAQ0dHRijZf3iejTcZ9VElJSYFUKlX6+lHIZXJEh8cgMS4pv0MhhBBCvgs5Sqr69++PR48eAQCmTp2KDRs2wMDAAOPGjcOkSZN0FtybN2+wadMmlC1bFhcvXsTw4cMxevRo7N69GwAQFhYGALC2tla6ztraWnEuLCwMVlZWSufFYjEKFSqk1EZVH1/eQ12bjPOqLFmyBObm5oove3v7bD3/9yghNgHbp/6DLlYD0a3YYHQw/w1T3Rbgyc3n+R0aIYQQkq+yVfwzw7hx4xT/39XVFQEBAfDz80OZMmVQtWpVnQUnCAJq166NxYsXAwBq1KgBf39/bN68GX379tXZfXLLtGnTMH78eMX3Uqm0QCdWCbEJGNtgFt4FhCrt9ffA0x/3rzzBrIPj0bBzvXyMkBBCCMk/ORqp+lJycjIcHBzQqVMnnSZUAFCsWDFUrFhR6ViFChXw7t07AICNjQ0AIDw8XKlNeHi44pyNjQ0iIiKUzstkMkRFRSm1UdXHl/dQ1ybjvCoSiQRmZmZKXwXZnvlHMiVUwL+bKTNgWb/1SIqn6UBCCCE/pxwlVXK5HAsWLICdnR1MTEzw5s0bAMCsWbOwY8cOnQVXv359BAYGKh178eIFHBwcAACOjo6wsbHBlStXFOelUil8fHwU2+W4uLggJiYGfn5+ijaenp4QBAHOzs6KNjdu3EBaWpqijYeHB8qXL69409DFxUXpPhltfpZteVJT0nBu++VMCVUGxhiSE1Nwdf/tPI6MEEII+T7kKKlatGgRdu3ahWXLlikt7q5cuTK2b9+us+DGjRuHO3fuYPHixXj16hX27duHrVu3YsSIEQAAjuMwduxYLFy4EKdOncKTJ0/w22+/wdbWFh07dgSQPrLVqlUrDB48GHfv3sXt27cxcuRIdO/eHba2tgCAnj17Ql9fHwMHDsTTp09x8OBBrFmzRmnqbsyYMbhw4QJWrlyJgIAAzJ07F/fu3cPIkSN19rzfs6iP0UiKS9bYRiwW4e3T93kUESGEEPKdycmrhaVLl2aXL19mjDFmYmLCXr9+zRhj7Pnz58zCwiInXap1+vRpVrlyZSaRSJiTkxPbunWr0nlBENisWbOYtbU1k0gkrHnz5iwwMFCpzefPn1mPHj2YiYkJMzMzY/3792dxcXFKbR49esQaNGjAJBIJs7OzY0uXLs0Uy6FDh1i5cuWYvr4+q1SpEjt79my2nqUgl1SIjohhrlwXjV9uet3Yjul78ztUQgghRKe0/fzO0d5/hoaGCAgIgIODA0xNTfHo0SOUKlUKz549Q926dTNVKyfpCvref6N/mY6Au6/ABPW/Mht8l6JcrdJ5GBUhhBCSu3J177+KFSvi5s2bmY4fOXIENWrUyEmXpADoPaur2oSKF/Go6VrlmxMquUyOW8d9ML/rCkxoOgcrB23CM+9A5CD3J4QQQvJUjkoqzJ49G3379kVoaCgEQcCxY8cQGBiIv//+G2fOnNF1jOQ7Ubd1DUzYPhxrft8GuUwOXsQDLD0Rqtq4ImYfnvBN/cdFx2Oq20K8uPcavIiHIBfw9HYALvzliVYDmmLc1mHg+W9+YZUQQgjJFdma/nvz5g0cHR3BcRxu3ryJ+fPn49GjR4iPj0fNmjUxe/ZstGzZMjfjLdAK+vRfBunnOHj8fR3vA0JhYGKARl1dUMG5LDiO+6Z+p7svht+lR2rfMBy4uCe6T/2f4vvYT1Jc3HkV/rcCAA6o3rQyWvZtAhML42+K40tyuRwAIBKJdNYnIYSQgkXbz+9sJVUikQgfP35UVCj/9ddfsXbt2kyVxolqP0pSlRuCn4dgUKVxGtuYFTHFgZAt0NPXw71LjzC30zKkJqeBCQzp+RwHAxMDLDozDVUaVvimeG4d98GRVafxzCsQjAFOdcugy/h2aNTV5ZuTR0IIIQVLrqyp+jr/On/+PBISEnIWISFf8Lv0CByvOVmRforDm8fv8PFNOGZ3WKpIqACAsfTfz5SEZExvswifPkTlOJbdcw5iXucVeH7nJTJ+5V/ce42F3f/E9in/5LhfQgghP7ZvWqBCi4eJrshlglYjQPI0GU5uuAC5TFC5aF4QGFKT0nB+2xUVV2ftmXcg/llwJL2vL6YhhX/vdWjFKTy86p+jvgkhhPzYspVUcRyX6YOPpkKILjjVLaN2LVUGPQM9OFQsDq+TvhrbCoKA2yfv5iiOU5suQiRW/5+FSMzj5IYLOeqbEELIjy1bb/8xxtCvXz9IJBIA6fv+DRs2DMbGyguDjx07prsISYHBGMPHN+FIiE2EtUNRmBU21frayg2c4FCxON4HflCZMPEiHm59m8DY3BhpqWkqelCWlirLVuwZXvi+hlymPmGTywS88H2do74JIYT82LKVVPXt21fp+969e+s0GFJweZ++h12zDuDN42AA6SM6DTrXw5A/esOqRNEsr+c4DrMOjcf4xnMQH5PwX2LFARw4lK5WEoP+SP99q+BcDrfD7kJQk/yIxDwq1iuXo+fQN9DPuo2hXo76JoQQ8mPLVlK1c+fO3IqDFFApSSnw3HsLq4ZsVpoKlssE3DxyB4+uPsWGu0u0SqwcKtpjy6MVOLnuPC7tvoa4mATYOBRF26Et0WaIKwyM0kdIO4xshZtH76jtRy4T0G54zkp71P9fXQT5v1M7vciLeDToVC9HfRNCCPmx5WibGpIzP0pJBcYYLu+5gSOrTitGptThxTyadPsF0/4Zo9MYds06gL2LjiqKhAJQ/P9BS3vj18kdctRvVFg0+pUfjZSEFMXi9Aw8z0HPQB9/PftTqySREELIjyFX6lSRb/MjJFWMMawbuR2nN10Cx3Ma9wHMIBKLcCRih06LcgLpU45H/zwD/9sB4ABUa1IJXSa0R+2W1b6p32d3XmCG+2LExySA53kwxsAYg5GpIRacmoqqjSrq5gEIIYQUCJRUfYd+hKTq7vkHmOG+ONvXbXuyCiUr2edCRLkjKT4J53d4wuuULxgD6rnXhPuQFjAyNczv0AghhOQxbT+/c7T3H/l5ndx4QWnKTVsmFka5FJHupSSl4K8Z+3Fu22WkJqe/afjc+wU+vA7HkOV9YGhskM8REkII+R5RUkWy5dX9N9lKqDieg5NzWRSxK5yLUemOXCbHrPZ/4OFVf6WpzbSUNJzbehlBT4Kx/Moc6OnTG4CEEEKUfVNFdfLzkRhKtG/MAWBAv3m/5lo8unbr+F08uPJETbV2AU9vB+LaQa98iIwQQsj3jpIqki0NO9cDL9L8a5Oxh5+xmRFmHhyHmq5VcyWWj0Hh8L3wAP63nkOWlrNin187v+OKxufjeQ7ntudsCxxCCCE/Npr+I9nS/nc3nNxwAWnJqapLDkj00GFka5Su5oD6/6ubvZEtLYW8/Ii1v2/DgytPFMcsrMzRe1YXtP/d7Zu2TgoLishiCxyGsKDwHPdPCCHkx0VJFckWa4eiWHphBma2X4qE2ETwPA+OSy+4aVbEDEvOz0CZGo65dv+wtxEY7TIdCbGJSsdjImKxftQOxEXFo/esLjnu39LGAh9eh6kvFcEBllbmOe6fEELIj4uSKpJtlRtUwP73W3B13y343w4AOKBG0ypo3M1Fq21evsWeeYeRKE1UO5q0Z/5htB7UHIWLWeao/xZ9GsP/5nONbVr2a5qjvgkhhPzYqE5VHvoR6lTlp6SEZHQq3B8yDZsl8zyHAYt75biiekpSCkbUmYr3Lz5k2ltQJOZhXdIKm/yWUb0qQgj5iWj7+U0L1UmBIf0UpzGhAtK3qol8/ynH95AYSrDccy5qNKuS6VzlhhWw6vp8SqgIIYSoRNN/5LuVnJiCS7uu4cJfV/DpQzQsrc3BcYCmsVVBYDAv+m2jgJZW5lh6YSbeB4bi8fVnYAyo0tAJDhULTkV4QggheY+SKvJdkn6Ow4SmcxD89D2A9EQqJjxGY0IFAIJcQPNeDXUSg315O9iXt9NJX4QQQn58lFSR79LqYVvw7nmoUhKVVULFcYD7kBawLW2Tu8ERQgghKlBSRb4bYW8j8NLvDeJjEnDr2F1o9Q7Fv1XbxfpidBzVGoOW9Mr1OAkhhBBVKKki+S4qLBqrhmyBz1k/IBvvovaZ0xWW1hYwNDFA3TY1YFbINPeCJIQQQrJASRXJV/ExCRjbYBbCgyOzlVABQMlK9mjUxSV3AiOEEEKyiZIqkq9ObriAsLcR6iuYq8GLeFRu4JRLURFCCCHZR0kVyVfnt1/JUULVrEcDFLJRrpqenJiCm0fvICTwAwxNDdGgkzOKly2my3AJIYQQtSipIvkqOjxG67Y8z0EQGJycy2LUhkFK524d98Hy/huQKE2CWE8EQWDYMW0vmvaojwnbh+t8Y+ekhGR4n/RFVFgMChWzxC8d6sDASPebRxNCCCk4KKki+crCyhwR79RXQOc4DhbW5jAtZIKidoXQakAzNOjkDLHef7+6j649xfyuKxVvC8rS5Ipz1w96QZALmHlgvM5iPrnhArZP/QfJCSngRTwEuQBDUwMMWfYb2g5tobP7EEIIKVgoqSL5qvXA5tgz7xAENVOAjDFM3jUStVtWUzouCAI+vAqDXCbHrtkHwHGcymlEQWC4fsgbbv0eoE6rGt8c77ltl7F+1I7/+v93Y+ekuGSsGb4VYn0xWvWnDZcJIeRnRBsq5yHaUDkzaVQcfq81BZGhnzNtYMzxHGq5VsWic9PB8+nbVDLGcHrTJRxcdkLjCJcqDbvUw6S/foehSc727ktLTUN3u6GQfo5T28bCyhz7329WGkkjhBBSsNGGyqRAMCtkitW3FmTawFgkFqH1wGaYe3ySIqECgE3jdmHdyO3ZTqgA4Pbxu5jzv+XaFRVV4aGnv8aECgBiImLx+MbzHPVPCCGkYKN/TpN8V8SuMJZemImQlx/x4t5riMQiVGtSERZFzZXaBd57jeNrz+X4PoJcwIMrT/D4xjNUa1wp29dLP8dr1+6TNNt9E0IIKfgoqSLfjeJli2ksgXB+22WIxDzkX00TZgfHc7i671aOkiprhyLatStple2+CSGEFHw0/UcKjHeBod+UUAEAExgeXH0CuUyedeOvVKrvhGKlrcHxnMrzHM/B3skWTnXLfFOMhBBCCiZKqkiBYWppopN+PrwKx4Yxf2X7Oo7jMHbzUPA8lymx4ngOPM9jzMYh4DjVSRchhJAfGyVVJF/J0mS4uOsqRtWbhk5F+qNv2ZH4e+4hREfEZmrr0r6Ozu57ZrMHPPffhNdJXwQ/D9H6uprNq+APj9koU8NR6Xi5WqWxwnMOqjXJ/rQiIYSQHwOVVMhDVFJBWWpyKma0XYKHnv7g+P/qTPEiHqaFTLDq+nyUcLJTtA99+RH9yo/OlVgqupTD2M1D4FjFQXEsLTUNt47dxc1jd5Acn4wSFYrDfYgr7Munx/Q+MBRRH2NQ2NYSxcvZ5kpchBBC8p+2n9+UVOUhSqqU7Zi2FweXn1RZtJMX8bAra4MdT1crptOS4pPQ0bKfouCmLvEiHhIjfaz3WYoSTnaIDPmMya7zEPLio2J7HF7MQ5AJGLCoJ3pM+5/OYyCEEPJ9+iHrVC1dujR9XcvYsYpjycnJGDFiBAoXLgwTExN07twZ4eHhSte9e/cO7u7uMDIygpWVFSZNmgSZTKbU5tq1a6hZsyYkEgnKlCmDXbt2Zbr/hg0bULJkSRgYGMDZ2Rl3797Njcf8KaQmp+LUpotqN1MW5ALeB3zAo2tPFccMTQzR5NdfwIt1/2sryAWkJKZi9+wDEAQBM9wX4+Ob9N+jjGrvGcVJ/5qxD9cPeek8BkIIIQVbgUmqfH19sWXLFlStWlXp+Lhx43D69GkcPnwY169fx4cPH9CpUyfFeblcDnd3d6SmpsLLywu7d+/Grl27MHv2bEWboKAguLu7o2nTpnj48CHGjh2LQYMG4eLFi4o2Bw8exPjx4zFnzhzcv38f1apVg5ubGyIiInL/4X9A7wJCkShN0tiGF/N4ejtQ6VifOd1gYCQBL8qdxOrW8bu4c/oegp68U/umIcdzOPDHCZ3fnxBCSMFWIJKq+Ph49OrVC9u2bYOlpaXieGxsLHbs2IFVq1ahWbNmqFWrFnbu3AkvLy/cuXMHAHDp0iU8e/YM//zzD6pXr47WrVtjwYIF2LBhA1JTUwEAmzdvhqOjI1auXIkKFSpg5MiR6NKlC/7880/FvVatWoXBgwejf//+qFixIjZv3gwjIyP89Vf23yIjgEibpIghU/JUvGwxrL61EOXrlFY6bmhiACNTQ7XlDrQlyAXcPuGrsR8mMLx6EIRYKvJJCCHkCwUiqRoxYgTc3d3h6uqqdNzPzw9paWlKx52cnFCiRAl4e3sDALy9vVGlShVYW1sr2ri5uUEqleLp06eKNl/37ebmpugjNTUVfn5+Sm14noerq6uijSopKSmQSqVKXySdvZMdLKzMNbYR5AJqNK+c6bhj5RJY67UY256swqxD47H43HQcDt+Of95uxOA/+qB09ZKwdiiKyg2cULamo4qe1eM4DiEvP6qdlvxSWqosyzaEEEJ+Ht99RfUDBw7g/v378PX1zXQuLCwM+vr6sLCwUDpubW2NsLAwRZsvE6qM8xnnNLWRSqVISkpCdHQ05HK5yjYBAQFqY1+yZAnmzZun3YP+ZMR6YnQe1xY7pu8FVOQvvJhHuVql4VS3rNo+SlayR8lK9orvJYYSdJ3QDl0ntFNq9+zOC4z5ZUaWMfEiHnVa18DT2+r/TDPoG+pjRO0piP0khYWVOVoPbI7/jW4Ds8KmWV5LCCHkx/Rdj1S9f/8eY8aMwd69e2FgYJDf4WTbtGnTEBsbq/h6//59fof0Xek6sR1cezcCAIj+XXyeMe1mW8oac45O1Ml9gp9q93PnRTxa9m2C+OiELNumJqUiKiwGcpmAzx+isW/xMfxeewo+hX7+1nAJIYQUUN/1SJWfnx8iIiJQs2ZNxTG5XI4bN25g/fr1uHjxIlJTUxETE6M0WhUeHg4bGxsAgI2NTaa39DLeDvyyzddvDIaHh8PMzAyGhoYQiUQQiUQq22T0oYpEIoFEIsn+g/8kRCIRJu8aiZZ9m+Dc9ssICfwI00ImaNazIZp2/wUSw+z/7ORyORJiEiEx0ldcL9Nymq7TWHfYlCya7XsC6VOVkaGfsWrwZiw+l/WoGCGEkB/Pd51UNW/eHE+ePFE61r9/fzg5OWHKlCmwt7eHnp4erly5gs6dOwMAAgMD8e7dO7i4uAAAXFxcsGjRIkRERMDKKn2jWw8PD5iZmaFixYqKNufOnVO6j4eHh6IPfX191KpVC1euXEHHjh0BAIIg4MqVKxg5cmSuPf/PgOM41GhWBTWaVfmmfhKkiTiw9ATObrmEuOgEcDyHOq1roNf0Tiiv5V58gXdfouf0TtAz0ENaclq2YxBkAnwvPsTHN+EoVso60/mk+CTcOnYXEe8/waKoGRp2rkfThYQQ8gP5rpMqU1NTVK6svFDZ2NgYhQsXVhwfOHAgxo8fj0KFCsHMzAyjRo2Ci4sL6tWrBwBo2bIlKlasiD59+mDZsmUICwvDzJkzMWLECMUo0rBhw7B+/XpMnjwZAwYMgKenJw4dOoSzZ88q7jt+/Hj07dsXtWvXRt26dbF69WokJCSgf//+efTTIOokxCZgbMNZePc8VFEYlAkM9y48xL0LDzHnyETwIj7LoqFPvV7A2MwILX9rgvM7ruSsyCgDXvi9yZRUndt+BZvG7URyQgpEYhEEuYD1o3agx7RO6DOnK+0XSAghP4DvOqnSxp9//gme59G5c2ekpKTAzc0NGzduVJwXiUQ4c+YMhg8fDhcXFxgbG6Nv376YP3++oo2joyPOnj2LcePGYc2aNShevDi2b98ONzc3RZtff/0VkZGRmD17NsLCwlC9enVcuHAh0+J1kvf2zDuslFBlEOQCOA5Y+ts6RSKjiTxNDgAYtLQXnnkH4u3T90pvAX65lY4mevrK/1l57r+FP4ds/u8+svT7yNLk2DP/MER6IvSa0VlxnjFGSRYhhBRAtE1NHqJtanQvNSUNXawGICkuWWM7Y3MjJMQmamwjMdTHmYS9AIDEuCQcX3sOZ7ZcwqeQKOhJxLC0sUDku0/Q9F+MnkSMQx+3w8TCGED6NHGf0iMRERyp8b67X63H5T03cHrTRYQHR8LQxABNu9dH14ntaV9BQgjJZz/kNjWEfO1zaFSWCZVYTwS7csWy7MulfR3F/zcyNUSvGZ2x5PxMWFqbIy1Fhsj3nzUmVBzPoe3QloqECgBe3HutMaECgJSkVIypPxM7pu9F+L9tk+KTcXHXVQyrORnPvAM1Xk8IIeT7QEkVKdAkRvpZtmGMoVqjSjAyNVTbRqwvxuA/eikdS4xPwqRmcxETmV60Vd3UX0Y5iF/a18HgZb2VzsVpUZ4BACKCIzP1L5cJSEtJw/yuKxVThoQQQr5flFSRAq2QjSXK1S4NXsO2MnKZgKY96mPtncWwtM5cxd3I1BArr86FVYn0cgqMMZzbdhl9y4xCTKRUbTLFcYCltTlcezfGymvzMOfoROjp6ym1UfUWoCrqZuEFeXodrDtn/LTqhxBCSP4p8AvVCek9qwtmd/hD5TlexKN600ooW7MUAGDfu83wPnUPD648gSAwVPqlPBp3c4G+QfqIF2MM60Zux+lNl7K8L2NAYlwyJv71u9Lx1ORUXNx1TbE+Sk8ihixVrjZxyopILMLL+29Qv2PdHF1PCCEkb1BSRQo8l3a1MWbTEKwfvQOCXADPpw/AymVyVGnohFmHJijaivXEaNi5Hhp2rqfUR+irj3h09SnePQ/RKqHKIEtTLiyaGJeEcQ1n4c3j4Cyvzagen9UbhXKZHPExmhfZE0IIyX+UVJEfQtuhLVD/f3Xhsfsa3geEwtDUEI27/YKKLuU0lieQfo7Dsn7r4XP2frbvyXFAmerKGzb/OXSzVgkVAJSvXRoDFvfEqsGbEfY2QuUeiBmu7L2J/gu7w9jMKNtxEkIIyRuUVJEfhqWVObpN6qB1+9TkVExqPg9vtdwb8GuMAf8b3UbxfXxMAq4f9Mryugl/DUe1RpUU6616TOukVMdKlfiYeFzecwMdRrTKUayEEEJyHy1UJz+tawe98OZxcM4qpwMoZGOBpj3qK773u/RIY8mFDC98XystYG89sBkkhprfYuQAeJ28q7ENIYSQ/EUjVeSHFftJinfPQ6EnEaNszVIQiUVK5y/9fU3rKulf43gOrn0aIT46AV6n7iE+Oh7Bz0K0i+vfEg2KvjgOIj0RkKT+GsbS61kRQgj5flFSRX44MZGx2Dx+N64d9FLUd7KwMkf3KR3Raay7Yo1VdHhsjhKqDMnxKfjVbghkqTKt9hbMUKaGY6ZjpauXxNPbgWr7EIl5xRuMGWRpMkSGfIaevhiFbQvR1jaEEJLPKKkiP5S46HiMqT8TYUERSglKTEQsNk/YjciQzxi2si8AwKZkUYQEfsjW9B/PcxAEhlquVXFq00XFca374IBWA5tnOtxhRGs8ufFc7WVymQADEwNsn/oPzIqYIupjDDz+vg7p5zgAgL2THXpM/R9c+zSi5IoQQvIJ7f2Xh2jvv9y3a9YB7F96XGOSs91/FRwq2uPWcR/M67xCY38lKhRHTEQspJ/jwPMcarlVR+uBzbCo++ocVzkvU8MRMw+Og12Z/7bOYYxhxcCNuLRLeUoy4/9nlF8QiXjI0jLfl+PSpwh7zeyMfvO75yguQgghqtHef+SndGarh8aESiTmceGvqwAAl/a1UatFVUXC8iVexKNCvbLYdH8Zjkb+hQtpB3Ah7SAWn52O6LDYHC9uB4CgJ8EY32g2YiJjFcc4jsOE7cMxYcfvcKxc4t9jgO2/C9qZwMAEpjKhAqBYIL934dEcv81ICCHk21BSRX4YaalpmRaBf02QM4QHRwAARCIR5p+cgo4jWyu9fSfWF8Otf1P8cWkW9CV6irYZ02rSz3HgRTn/T0cuExATKc1UZJTnebTq3xRbHq7AueR9OCndA2lUfLb65sU8zm71yHFshBBCco7WVBGdex8Yisj3n2Fe1Aylqjrk2RofsZ4YEkN9jW/J8SIOZoVMFd/rG+jj99X90Xf+rwj0fQ0whjI1HZXafM26ZNFv3uBYkAu4tPsa+szuqjgW8uIDjv55BtcOeiE5IRmWxSwRl82kSpAJePc89JtiI4QQkjOUVBGdCbj7EhtG/4WAu68Ux+ydbDF0+W9wdq+V6/fnOA6uvRvhwk5PyGWqp+fkMgHNejXMdNzYzAg1m1fR6j71/1cXokEiyNVMxWkrY5E5APjfeo6pbgshS5MpYo989ynbffI8DyMzw2+KixBCSM7Q9B/Riec+LzG+8Wy8uPda6XhI4EfMbL8UN45450kc3SZ3gL6hvsrpOV7Eo1aLqqjSsMI33ePa/tvfnFBxXPqIFwCkpqRhbqcVSEtJU5sMaksQBDTp9ss39UEIISRnKKkiOrFh9F+Qp8khfFX3KePl0rUjtmfafDg32Ja2wapr8xUVy3kRnz79yAGNutTDnGOTvmk6kjGGQytOpZc4/0bug1sAAG4fv4vYT9JMP7vsEol52DvZ4ZeOdb49OEIIIdlG03/kmwU/D0Gg7yv1DVh6FfF7Fx+hXlvtpgHDgyNxYt15XD+Uvr7IoXIJtB/uhsbdXMDzmv8tUKaGI3YGrMGja0/x+uFb6BvooU7rGrApaZWdx1IpJiIWoS8/flMfHAeUq10arQY0BQAE+r6CSC9n04kcz4HjOAhyAaWqOmD+qanQ09f7pvgIIYTkDCVV5JtFaLH2h+M4hAdHatXfM+9ATHVbiJSkVEXpgmdegfC/+Ry3jvtg+r4xEIlEGvvgOA7Vm1ZG9aaVtbqntnRR1o0x4PWjYOyYtg9dJ7aHnr4Y0KJbjuPS788BhYtZIiosJr3UAhgMTQ1Q/3/OsLQ2/+b4CCGE5AxN/5FvZl5E/ZtyGRhjWrVLTUnD7A7LkJKYolQLKuP/3zxyByfXXch5sN/I0tpCsRbqW8hSZTix7jyG1ZiEEhWLa3ybkOM4lKhgh8Ph27HOZwnsy9tm2mInKS4Zu2YfwOqhW3SS+BFCCMk+SqrINytbsxRsy9hoXGdkYCyBsxZTfzeP3NG4vogxhqNrzkIQVC/oTk5MwfkdV7By4EasGrwZnvtvITUlTavn0AbHcegyrp1O+mKMIT4mAUdWnUaZmo7gxar/c2SMofvU/8G8iBkeXX2KkBcfVRcfZcCFv67imfcLncRHCCEke2j6j3wzjuMwZFkfzO20PD2xUpEP9Z33KwyNDdT2wRhDwN1XOL35otI2LapEBEciNlKKiPefcf3gbcRFJ8C2tA1KVCyOFQM2ID46AaJ/E5TzO66gUDFLLDk/A6WqOnzrowIA2o9wQ+C9V7i850a2NlJWRZALePMoGAtOT8W2yXvw7nmoYn9BkZiHXCag68T2CAuKQM8SwxAZ8jnLPsc1mgXX3o3Qfer/UMLJLsexEUIIyR7a+y8P/eh7/109cBvrR+2A9HOcIjEyMJag77xf0XlcW7Vv3YW8+IC5nZYj+FmI1veq7VYN9y4+gkicvrZKEIT0RExFUseLeJhaGmNn4FqYWprk9PGUMMZw54wfTm+6iKAn7wAAn0KjctYZB4xePwitBjaD1wlfXD/ijcTYRJSoUBz1O9bBioGbEBEcma23A0ViHmJ9PSy/MgcVnMvmLC5CCCEAtP/8pqQqD/3oSRWQvlXM3XMPEPHuE8yLmsGlXS0YmqgvRhkVFo3BVcZD+lm7yuEcx8HQ1ADJCSnZGiHieA6D/+iDrhN0M3X3tT3zDmPvoqM5rrQ+YftwtBrQLNPxGe2WwO/iwxzVr+JFPIoWL4y/X69XvCggyAVYlSgCsV76ILUsTYZn3i+QEJuI4uWKwb48jWwRQsjXtP38puk/olN6+nqo37Gu1u1PrDuvdUIFpI8QJUqTsh0XExhuHruTa0mVnkSc4wXiHM+hVstqmY6HvY3A3XP3tXozUBVBLiA8OBKbx+/GnTN++PgmHABgVsQUHUa0gllhU+xdeBQxEf9t7Fzxl/IYu2kwHKvoZqqUEEJ+JrRQneSr8zs8tWqXUSG9WpNK4PmcVd5MSUjJ0XXacHavmaO1VbyIR/OeDVG0eOFM5176vclxQqXAAcfXnsPHoHDFIemnOOyZfxgbRv+llFABQIDPS4xpMBPvAmj/QEIIyS5Kqki+io9O0Kpdpfrlsd5nCao1qQQui+KfqojEIpSuXjLb12nLsYoDareqrnJ7HE0qN3DCmM1DVJ4T6+tgIJl99b9fH/+KIBeQkpiKnTP2ffu9CSHkJ0NJFclX5kW1W1v2v9HuKF+nDEpWss/RuiW5TI52w92yfV12zNg3FhXqpS8KF4l5cNx/I2yqyk3wPIeYiFi1I29VGlaAniTvq6MLcgG3T/pCGhWXdWNCCCEKlFSRfNVxVKss24j1RajpWgUA4NK+NsyLmoHTcgowo13P6Z1y/S04EwtjrLo+H0svzkSL35qgQSdnVPylvNoyE4LA8O55KK4fUr3ZtImFMdoOa6H1s+oSExiiw2Ly/L6EEFKQUVJF8lWHka2z3Fql89i2MDYzAgCI9cSYvncMRGJRpqk2XsTDvIgp7MraKI6Vqe6IGfvHov/CHroPXgWe51GrRTVM2D4csw9PREx4rMZ1URzPwWPPdbXnB//RG7+0T98gOaP2lrZTjHoSvZwnZBxgVuTHfEOVEEJyC739R/KVobEBtj/9E+MazsK755kXR7ce2Az9FyknRDVdq2LN7YXYt+gYvE/5QhDS975rM7A5ekzvBPMiZkiMSwLHQWM5h7wQl8UUGhMYYiOlas/r6ethztGJeHzjGS7tvobQFx/x/E7WFdOL2Fmi2+SO2DhmZ7Zj5kU8ajavAkurzMkuE6IAeSTAFwYnKpLtvgkh5EdGSRXJd2aFTLHj6WoE+L7CibXnII2KR8lK9nDr3xQOFYqrvKZcrdKYe2wSUpJSkBSfDFNLE0UhUAAwMs3fZCqDjaMVpFHxaivE82IedmWLaeyD4zhUa1wJ1RpXwvpRO/DUK1Bje5GYx7o7S2FayBgn1p1HWFBEpjcTOS59Y+dM9+I58CIe/b4a2WNpgWBxK4DUG8gYemP69cGZjgenV0VjPIQQ8rOg6T/y3XCqUwZT94zG4rPTMWRZH7UJ1ZckhhJYFDVXSqi+J22HttS45Y4gE/Dszgu4G/dEF6sBWNRztVL5g69pmirMIJcJeP3oLcR6YpSp4aiy1IPESII2g5pDYqivdLyYoxWWecxG+dqlFcdY2jOwz92A1FtQmstM9Qb73AMs1S/LmLSVmpyKkBcfEPEukjaGJoQUOFRRPQ/9DBXVibK01DRMdp2PZ94vslXHqmytUnBuUxN1WtdABeeyii1+3PR+1aqfln2bwKywCY7+eVZtctJ/YQ90GNkK9y48RII0CXZlbVC1UcVM2wkJnzoBsmcAMt+XMQ4CVwJi60tqtyHSRmJcEv6ecxDndlxBUlwyAMChYnH0nNEZzXo0yHG/hBCiC7RNzXeIkqqfU3JiCnZM24vzOzyRkphegFSsJ4IsLYvSEP++NehUtwzmHJuEIraF0KvkcES8+6TVfTM2ZFbH2NwIBz9shcRQorYNSwsE+5x1Ffo96zqh86RZMLEw1iq2LyUlJGN8o9l48zhYKWHkOA6MMQxa2hu/Tu6Q7X4JIURXtP38puk/8tNjjOHu+QeY879lGFBxDMbUn4FTGy8iMS772+GoYmAkwYg1A3A4bBvW3F6IxeemQ67NqNW//9x5ef8NJjefh9TkVPxvTBut75vVfoEJsYl4elvz+izI32p1r4g39zDZdT5Sk1O1jO4/x/48i9eP3mYagcv49972qf/g3PbL8PN4hJSk3KuKTwgh34oWqpOfmlwux9Lea3HtoJdiZIfjgGd3XuDQipNYeXUerB2K6uRehiaGqOhSHnfPP9C4zipTjDIB7wM/4MaRO2g7tCXOb7uis21kkhOzSFI4U636KVwsBZcOvkGPEsNgZGqIms2roOOo1lnuIcgYw6lNF7P8efw5ZAsAwMjMEF0ntEfPGZ3A56CyPiGE5Cb6W4n81A4tO4Xrh7wA/DeywxgABnwK+Yy5nZbrfMF0Rr2p7OB4DlcP3IaBkQTrfJbAsUoJncSyZthWjP5lBo6vPYfkxOTMDfRrAzDS2AdjQKU66dsNST/FISwoAhd3XcXQ6pNwcddVjdempaQh6mO00rEixVLRc2w4Jq19hxGLQlDZOR4Zw3aJ0iTsnnMQG0b/pfUzEkJIXqGkivy0ZGkyHF19RmVpASA9yXr1ICjLEgbZVaFeOeh/9dZdVpjAEB8dDyC9XESfOd10EktUWAye33mBjWN3ooN5X2wY8xfi/r0PAMRFp+DzZwfINSz/4jigev0EfPlmoFwmgDGGFQM3YuWgTTi37TISpImZrhXriyHW++/NzU5DIrHn7nP0nhCGph2j0ab3Z6w8/hrLjryGkel/QZzaeBHBz0O+7eEJIUTHKKkiP633gR80Ft4E0keVHl19mqP+01LTcP2QF7ZP/Qe75xxE4L3XANKTog6/Z28fQpGYh72TneJ7l3a1tN43UVuCXMCJ9ecx2mU6PP65jhF1p6JzkQHoWUUfPapXxD8rrZGarPoNP16kJjNlwMVdV/HnsC34tdhgXNipPHLF8zwadXWBSMyj++gwDJ37AbwIEIkAkRgQ/7v1YeW6CZi+OVhxnUjM49Kua7p4bEII0RlaU0V+XlpO6+Vk+u/RtadY0G0VYj9JIdYTgTGGfxYcQdVGFTH7yAT0X9QDQf7vcO/iI636k8sEuA9pofherCfGlN0jMcN9iW6nJxkQ+jIMy35br7TRc+xnPfzzpzUe3jbB4v1voC/5YlRKDrx4aASVu0YDivVSKUmpWDlwI0wtjVG/Y13F+V8nd4R98aPoOTYcjKWPfH1NJAbqNI1DqYpJePPMEIwBn0I/6+aZcyDI/x0+hXyGhZU5ytRw/KZyEoSQH8d3PVK1ZMkS1KlTB6amprCyskLHjh0RGKg8FZOcnIwRI0agcOHCMDExQefOnREerlw88d27d3B3d4eRkRGsrKwwadIkyGQypTbXrl1DzZo1IZFIUKZMGezatStTPBs2bEDJkiVhYGAAZ2dn3L17V+fPTPJO8fK2MLXUXAJALhNQpWGFbPX79ul7TGuzSLFFjSxNrliv5X87ANPbLIZILMLiczMwaecImBfNejF4+9/dMm0IXadVDUzaPTJbsWkjI0kTvlo8zgQO/j7GOLO7sNJxkQg4vk27LWs4jsOu2QeUEkHHcoHoOTbs3/Ma4hIAl1axin5UbaOT257cfI5hNSdhSNUJmN5mMX6vPQUDKo6Fz7n7eR4LIeT7810nVdevX8eIESNw584deHh4IC0tDS1btkRCQoKizbhx43D69GkcPnwY169fx4cPH9CpUyfFeblcDnd3d6SmpsLLywu7d+/Grl27MHv2bEWboKAguLu7o2nTpnj48CHGjh2LQYMG4eLFi4o2Bw8exPjx4zFnzhzcv38f1apVg5ubGyIiIvLmh0F0Tk9fD+1HtFK76TAv4lGigh2qNq6YrX4PrzgFQSbPlJQA6VNsL+69xr2Lj8BxHFr2bYI9bzaiYWdnlRslF7a1xIi1AzBy3UAAQHhwJHZM34eR9aZhpPNUBPu/Q712tfNspIQBOLUzPYGS//vvkhM7CuP6KQvtrmcMb/3fI/Tlx/+OJeyAVn8VcUD1X+L/vbccrn0aZyPyb/f4xjNMaj4Pbx4HKx0PffERs9otxe0T9I8sQn52Bar4Z2RkJKysrHD9+nU0atQIsbGxKFq0KPbt24cuXboAAAICAlChQgV4e3ujXr16OH/+PNq2bYsPHz7A2toaALB582ZMmTIFkZGR0NfXx5QpU3D27Fn4+/sr7tW9e3fExMTgwoULAABnZ2fUqVMH69evBwAIggB7e3uMGjUKU6dO1Sp+Kv75/UlLTcPczitw9+x98CJeUSuJ4zlYWJlj1bV5KF7OVuv+GGNoa9wLqclpatuIxDya926ESX+NgFwmx+QW8+F/MwCCoFyniRfxcKhUHGu9FsPASAKvk75Y0G0lBIEp4uRFPDiOQ/VmlfDgin+2qrZ/izPBrxAXVwLPH9XFgj4PwbJ523V3FsOpblkwJgML1z5pjZfy6Fa5Chp1bYDpe8dkM+qcY4xhSLUJCH4Worr8AwcUsrHEvnebIBJ9n1smEUJy7ocs/hkbmz70X6hQIQCAn58f0tLS4Orqqmjj5OSEEiVKwNvbGwDg7e2NKlWqKBIqAHBzc4NUKsXTp08Vbb7sI6NNRh+pqanw8/NTasPzPFxdXRVtVElJSYFUKlX6It8XPX09zD8xGTP2j0WVhk4obGuJkpXsMWBhD2x/sipbCRWQPhKlKaHKaJMoTS8sevvEXTy+/ixTQpXR7u2Td/DYfQ0fXodhfreVkMnkSomTIBcgl8vx0PMpVl6di2KlrNUtbdIZsb4Y+sXvo0jFk2jYYwam7hkDXsSDz0apiOX9NyAy5DOU9hLUgomZgF7T62HyrhHZjPrbvH74Fm/936uvp8WAqI/ReHDFX/V5QshPocAsVBcEAWPHjkX9+vVRuXJlAEBYWBj09fVhYWGh1Nba2hphYWGKNl8mVBnnM85paiOVSpGUlITo6GjI5XKVbQICAtTGvGTJEsybNy/7D0vylEgkQpNf66PJr/W/vS+xCFYlimjcSobjeRQvWwwAcGHnVaURMlWOrj6Ls9suQ5DJVecgDGBguH7IGz1ndMbKgRs1xljUvjAi3+dskbdIzKNRl3pK043NejRA2ZqOOLXhIvwuP0bIiw9ZFvN8H/ABI+pMwdbHKyH7ZAdzy1BoO8DTtHsDiPXy9q+uiPfabQ2k7RZChJAfU4EZqRoxYgT8/f1x4MCB/A5Fa9OmTUNsbKzi6/379/kdEskmQRDge/Eh9i48igNLj+PVw6Asr2k33E3tOq2MPlsNbAYAiHz/SWNCxRgQ+vIjXj98q/FlRUEmwPfiAzTv1QAVfymvcn0Wx3Oo6VoF25/+iVHrB/63Tx/37zQiz4EX8bC0Nle5RovjOHAch64T22c6Z1/eDiPWDsBfz1ZjzpGJ6c+vadE5Y4gOj0VX60HYOkekVUIlCEBEqB7GNNiIiHeRWV+gQxZalq/Qth0h5MdUIJKqkSNH4syZM7h69SqKFy+uOG5jY4PU1FTExMQotQ8PD4eNjY2izddvA2Z8n1UbMzMzGBoaokiRIhCJRCrbZPShikQigZmZmdIXKThePQxC37KjML31IuyZfwg7Zx3A8JqTMbH5XMR+Uj+V23FUa5StWSpTYpORp/Sb3x12ZdJHqorYFlIqXfAt5DIBevp6WHphBloPbAax/n+jOfoGeugwohUWnJoKIxNDtP+9FY5H7cJ6nyXoMq4dWvVvikFLemGD71JwPK+yTANjDCPXDUSZ6o4a46jfsS6WecwGp+U85NXjFoq3BzUljhwHHNlcFAnSZBxafkqrvjWJDPmMp16BeB8YmmVZCifnslluV2RiYYzabtW+OS5CSMH1XU//McYwatQoHD9+HNeuXYOjo/Jf5rVq1YKenh6uXLmCzp07AwACAwPx7t07uLi4AABcXFywaNEiREREwMrKCgDg4eEBMzMzVKxYUdHm3LlzSn17eHgo+tDX10etWrVw5coVdOzYEUD6aMOVK1cwcqTuX2kn+S/iXSQmNp2LpPj0rVu+3Jz4yc3nmNJiPtbfXapyGsrASIIVnnOwZ95hnN12WbF+yt7JDj2mdYJr70aKti37NcW9S9rVqtJEJOZRrUklAOl7DI7dPBQDl/TCS783AMehfJ3SMDbLvN1M+TplUL5OGcX3M9otQWxkrMp7cDyHs9suK9XLUic9Fm3XS3HYPMcWdz3N0HV4BKrVj0fGtn4cB8jS0ouAXthXCKf+KgLGBFz4yxOfP0bjgecTgAFVGlVA57FtUb1p5SzvFvQkGJsn7Mb9y08Ux0pXc8DApb1Rx626ymt4nsfQFb9hfteVavsdsLgn9A2yVymfEPJj+a7f/vv999+xb98+nDx5EuXLl1ccNzc3h6GhIQBg+PDhOHfuHHbt2gUzMzOMGjUKAODl9e9+bnI5qlevDltbWyxbtgxhYWHo06cPBg0ahMWLFwNIL6lQuXJljBgxAgMGDICnpydGjx6Ns2fPws0tvfL1wYMH0bdvX2zZsgV169bF6tWrcejQIQQEBGRaa6UOvf1XcGwevwvH153XODU3+/AENOxcT2M/qcmpiHj3CXoSPViVKJJpWk2WJsP4xrMR6Pv6297c44CNvn+gbM1SOe4iPDgSvUv9nmUutPGedvfpX2EMQl58yO5adBgay9G8SzSadIyGiZmA4EAJzv1TBI+8jKF2TpEHIAADl/RC9ykd1fb95nEwxtSfgdTkNKWfN8dzAANmHZ6Ahp2c1V7vuf8WNoz+C9LPcemhsPRNngct6YV2w7NXJZ8QUnBo+/n9XSdV6mrv7Ny5E/369QOQXvxzwoQJ2L9/P1JSUuDm5oaNGzcqTcsFBwdj+PDhuHbtGoyNjdG3b18sXboUYvF/owzXrl3DuHHj8OzZMxQvXhyzZs1S3CPD+vXrsXz5coSFhaF69epYu3YtnJ3V/wX8NUqqCo6u1gMRo2ELG17Eo37Huph9eMI33ytBmog1w7bi2iEv5QXe/35oa5KxyH3kuoHoMKLVN8Vx54wfZrVfmmW7CduHo9WAZlm2O7H+PDaO+UvbwvU6s+b2QlR0Ka/y3MRmc/Hk5nPVCSwHmBc2xYHQrUojkIwxfAqNQlpKGooULwyOA3wvPMSnkChYWJnB2b0mJIaSXHoaQsj34IdIqn40lFQVHG0MeyAtRaaxTfVmlbH88hyd3fNT6Gc8uRkAJggIexuBnbMOaE6qOKBxt1/QaYw7KtYr98339/N4hKluC7NsN+2f0WjWs2GW7dJS0zCz7RI8uPIkTxOrcrVLYcPdPzIdD3sbgT6lsi7FMPfYJMU2OtcPeeGfhUfw1j/9JRNjcyO4D3ZF79ldYGhiqNvACSHfLW0/v7/rNVWE5BebklYIefFR7QJmkZhXLDbXlSJ2hdG0e3pZh8S4JOxbfBypSakqY+BFPLqMb4fBf/TO8f0YY3j1IAghLz7CyNQATvXKwcjUEIlxSWqvEYlFqNmiqlb96+nrYcHpaTj4xwn8PfdQjuPMrhf33uDlgzfgOA7FHK1gbJ7+lmNYUNa7H/AiHh/fpL+QcnjFKWydvEdpxDwhNhFH/jyDh1f9seLaPBgaG+TOQxBCCqQC8fYfIXktq/UxcpmANoOb59r9jUwNMeXvUYpyB1/iRTwcK9uj18zOOe7/hd9rDK85Cb/XnoLFPVdjZrul6FtmJJy+2l/wSxzPoc3g5rAoqv2ee/oSPfSZ3RVDV/bV2E6sJ8L8k1MgMZLopHjp77WmYHjNyehiPQjL+29AdHgMzApnvceiIBdgVtgUYW8jsG3KPwAyb6gtyAW8fBCE42vOqeqCEPITo6SKEBXch7jCybmMynpPANBhZCuUq1U6V2No2MkZq67PR223aopyDGaFTdFj6v+w6sYCGJnmbPop6EkwxjeegyB/5bppCbGJuH/5McrUTH/LViRO3wJHJE4vIlW/Y10MW9UvR/fsMq4tek7vpPZ860GucGlXG4vOTIOeRA+ibFRn10SWKsPlvTcwymU6LG0sULxcMY2bNutJxPilQx1c+MtTY60xJjCc2nRR7XldksvkuHnMB8sHbMCiHn/iwNLjiA6PyZN7E0Kyh9ZU5SFaU1WwJCUkY9fMAzi3/TKSE1IAAEXsCqHbpA7oOKp1nm1iDADJiSlISUyBiaXxN+8tN7vjH/A5e1/t24YiMY/F52fA58x9RIZ+hkVRczTv1QCR7z/j4q5riHj3CYVtLeHWrykada0HPX29TH0kxSfhmfcLyGUCSlcvicLFLHH/yhNMdVugttr6qPWD0P53NwQ/e4/DK07h6kEvpCalwtqhKMrWLIVbx31y/My8iEf74W6o1rQS5nVeobZdn9ld8dvcbljYfRVuHLmTZWX4s0n7oC/J/Py6EvEuElNaLkDIi48QiXkwgYEh/XkmbB+OFjreVJoxhud3XuD8Dk+EvY2AeREzNO/VEHXb1KA9DclPjRaqf4coqSqYkhKSEfryI8R6Ytg72RboDxdpVBy6FB2osdglx3MYuvw3dB7XFkD6889wX4wnN54r3jbkeQ6CwFC+TmksvThLUZ1dlibDrlkHcGL9BaQkpij6a9jJGcHPQvAuIFRtomJoaoBDH7fDwCj9TTrGGARBgEgkQlJ8Eno5/o6EmMQcl54wMJbg2OeduLLvFtYM2wpZqvKLCKaFTbDo7DQYGEmwY9o++F54AEGu/uck0hPhXNI+8LzyqNqrB0E4t/0KPrwOg1lhEzT5tT6c3Wtm+/dGLpNjUOVx+PgmXKlOWgaO47Di6lxUbaT9htRf+vgmHO+eh8DA2AAVfykHXsRj1eDNuLTrGkRiHnKZoPjzrvhLeSw+O02xPo2Qnw0tVCdERwyNDbKsIl5QxERIs6weznEc3j79b2pw49ideHo7EAAUCY3wb2L08n4Q/hy6BbMOjgdjDEt7r00f4fniHkxguHnMJ8tRn6S4ZNw5fU+xByPHcYpExNDEEEvOz8RUtwVIiEnM8hlUSU5IgfRzPO5deAhZWuY3O+Oi4jG63gyt+srYA/HLhIoxho1jd+LEuvNKScnV/bdRvk5pLD4/A2aFsl7XlcH79D2EvPiovgEH7F10NNtJVeirj1g9bCseev63+bNZYVOUq10a9y4+BPBfsduMP+8An5dY1m8D5h2fnK17EfKzoTVVPzHGGBJiE5CakpbfoZA8YmFlluW0pSBPr1g+u+MfCHsbjst/X1c7OiTIBdw8cgcR7z/hyc3nuH7YW/UWN1kkVED6iFZUWIza8+Vrl8bul+swZHkflKxsD7Fe9kZ+OJ7Dh9dhuH7IS+3G1Nr2w/E8fp3cUen48bXncGLdeQCZk5KX94OwuOeabMV757SfxrVlTGC47/EYg6qMR+C911r1GR4cidEuM/D4+jOl49LPcYqEShVBLsDrpC8+vA7T6j7fK0EQ4HXKFzPaLUG/cqMwymU6Tqw7r/GNV0Kyg0aqfkIpSSk4+udZnNxwAVEfo8FxQC236ug5rROqNKyQ3+ERHUmITcC1g14ID46EWWFTNPn1FxSxK4x67WppXFOVwefsfYS9jYQsTa6xHWMMT248h9/lR4oRmpxgAkMRu0Ia25gVMkWX8e3QZXw7yOVyPPT0x8c3Ebh+2Etp5EWVuq1r4PbxuzmLkQPEYhFkaXKYmBtj5sFxKF2tpOK0XC7HwWUn1V4uyAX4XXqEIP93cKxcQqtbpqakapWMBj99j5F1p2LGgbFo0q2+xrZ75h9GQmxCjqZQOQ7wu/QItsPV73f6PZPL5FjY/U/cOuajmNbE6zAE3H2JI6tOY+W1eVnu70hIViip+smkJKVgSssFeOb9QvEXNmPAfY/H8Lv4CNP2jlHUSiIF1+nNl7B5/G6kpqRCLBZBkAvYOnkPOo5qjb5zu+G+x2Okpco0frgKcgFBj4O1uh9jDBHBn3KcUAHphTXrta2ldXuRSIRaLaohNSUNO6btzbJ9g07OuHHYO8drsjqMao2yNUqhYWfnTHv8vXsWgqiP0Rqv5zgOexcdxcQdvyvWjWlSqmpJXDvopXV8S3qtQdVGFVHIxlLl+dTkVHjuu5nzPyOOyzLBBtLXat09/wBpKWkoU8MR1ZpUytOXOtTZt/gYbh+/C+C/EcSM0clPoZ8xr/NybPD947uIlRRcNP33kzmy8oxSQpVBkAtgjGF5/w2QRsXlU3REFzz338La37chNTkVYIAsTQ5BYGACw/G15+C5/xZW3ZiPkpXss+xLJOazrhvFAZV+KY9CNhZqS1BoY9jKvjnakDj46XvExyRobsQBu2cfhO+Fhzmr7s6AbpM6oHmvhipjTEvVXH0fSE88rx/0Qrdig+Cx53qW7VsNaApRNn6egpzh5PoLas9Lo+Kz3CVAE/bviwnqJMUnYcGvK/Fb2ZHYMPovbJv8DyY1n4f+TmPw6mFQju+rC2mpaTi+9pzatXhymYCX94PwzPtFHkdGfjSUVP1EBEHAyY0XNE4pyFJluPz3jTyMiuiSIAjYOXO/+gYMOLb6LKwdimL17ay3pGECg5V9EbXJEi/m4dymJoqVsoZrn8Y5HgWyciiq1X6CKmPUJktiwOcsRpKyYmJupPacfXlbSAy1SwiT4pKxrO96eJ3y1djOoqg5Jv6V9bY6X7p74YHacyYWxjmu/yUS8yhV1QEV1GyHxBjD7I7LcOvYXYD99+YmkD5yNbHpXHwMCs/RvXXhfcAHxEXFa2zDi3g8uvY0jyIiPypKqn4i8TEJiNawEBgAeBGHoCfaTfmQ/JOcmILTmy7i9zpT0MVqQPqX9UD0Kvl7ltuxyNLk8D51DwZGEpgXyeJtNI5Do64ucKxSAuD+2+Sc4wBwgF2ZYpj41+8AgNpu1VC9aeUcjVZFBEfm+EPXoWJxGJllXQhVm/VJ6hS2tdQ4imZoYgi3/k2z9eybxu3KMiFs3qshKtVXvTm0KpoGFQ2MJGjQuV62/3x4EQ8TC2PMODBO7dTYw6v+eOjprzKpFuQCkhOScXjF6WzdV5e0fVu0oFUYEgQBH16H4X1gKNJS6YWj7wElVT8R7aZWOOhr+S9ukj+kUXEY7TIda0dux0u/N4j9FJf+FSnFp5DPWV7Pi3gkxCaC4zi0HdpS44esIAhoP9wNa24vxNhNQ1C2VilY2ligdHVHjFw7EBvuLlFsW8PzPOafmoKm3etnrkauxTKVhNjErBupIDGUoP1wN40V0L/VwMU9s26zpBdKVXXQOo6woAiMqDsVMZGxGttlFJ7VRo1mVTSe7zOri9ZvTfJiHpY2Fug6oR02P1yBEk52att67rulcRRMLhNwWYspz9xi72SnqKWmjiAXclzzK68xxnB600X0KT0SfcuOwoAKY9Gt2GDsnLk/fdqf5BtKqn4iBkYS1GimeSRBLpOjfse6eRgVya7VQ7cg+FmI1iUAvibIBdiWSX+Dq+vEdrArWyzz78S/ecFvs7uhWClrSAwlcB/SAhvuLsWhD9uwyW8ZOoxoBUMT5REiQ2MDTN0zGvuCN2HyrpEYv20Y+i/qkWWsIrFI7ZtXyYkpiI9J0DiK0GduN9RyTd/o+ctn4XgOYv2s38cR64vVjnb1nt0FLX5rkmUfRqaGWHVjPgb/0QemhUyybA8AL++/wdSWCyCXqV8AXrR4Ya1GlzieQ9thLTW2cahoj5HrBmbZFy/iMWhxLxz6sA01Xati9dAt6GjZF/8r3A8Lu6/CM+9ApfZxUXGQZzH1mxSfDLk8/TnfBYRi7Yjt6FFiKLraDMKsDkvh5/Eoy7hySl+ihw4jW6kdacuY3qzcwCnXYtClTeN2Ye2I7YgIjlQci49OwP6lxzHDfQmNWuUjSqp+Mj2md1KsdfgaL+JRtqYjqjernMdREW1FvP+EW8fu5njtEsdxsLSxQN3WNQAAxubGWH1rAVoNaAa9L7ZbsXYoivHbhqH37C45uk8Ru8Jo8VtjtB7YHJ3HusPY3EjtaJVIzKNR13owtVRORO5deoSJzeainUlv/K9QP/R0GI6Dy06q/MDQl+hh4ZlpmL5vLKo0dEIRu0IoVdUBg//og2Y9G2gcReE4DvZOtjgRvRvjtg5FhXplUbp6Sbj1b4rD4dvRd+6vWj+3obEBuk5ohzEbB2t3AQNePwrG7RN31TZp2a+pVn/eZoVNcf2wd5Y1l+r/r26Wa6sEuYDSNRyxd+FRTGm5AL4XHiIhNhHx0Qm4dcwHYxrMxNmtHor21g5WWS6qt7SxgEgkgvfpexhabQLObvPAp5AoxETE4u75B5jqthA7pu/L8jlzqtfMzqjTJv33/uvE29LaAnOPTVJKuuRyOe5deoSzWz1w44g3khKScy227Aj0fYXja1Vv5s0EhodX/XFp17W8DYoo0DY1eeh72abG4+/rWDV4E+RyARzHgeM4yGVylKnhiMXnpsPS2iLfYiOaXT/sjYW/rsrRtTzPgeM5zD85VZFUfSlBmogPr8Kgb6AHeye7TNuvfIvrh72xqPuf4Lj/qrED6QmVeREzrPNZAiv7IorjZ7d6YPWwrf/VE/oXx3Oo0awyFp6ZpnLPQVVe3n+D32tPUd+AA8ZsHIK2Q1tk/8HUSE1JQ3fbwYiLSdBqRLGIXSH883ajyq1s5DI5JjWfh6degVkmVxzPoUSF4lh1fR7A0pNwU0uTTKOAy/qvx5V/bqrsjxfxsHG0wvhtwzCx6Vz19+I4bHuyEg4V7RHk/w5Dqk5Q25bnOfSa2QXthrdEb8ffkZaSpvYtzHknJuOX9nU0Pqc6sjQZrh/yxrntlxER/AmWNhZw69cEzXs3goGRBHK5HLeO3cXZrR4IefERpoWM4dqrEVoNbKaU1Pucu4/VQ7fgU2iU4pihiQH6zOmGLuPb5mvZhVWDN+PS7qtqS2NwHIdS1Ryw+f7yPI7sx0bb1BC1WvzWGHVaV4fH7usIevoOBoYS/NKxLmq6VtHpBynRvW/5u7xSAyf0X9BDbYFXYzMjlK1ZKlt9RkfE4ur+W/j8IRqFi1miaY/6KpPymq5V8L8xbXDzqA8i338CAIj1RGjSvT4GLOqJosULK9p+Cv2MtSO2A0CmD30mMDy44o/Tmy6h0xh3rWIsW7MUuk5oh8MrT6ePln3xYc7xHKo1rgS3/k2y89hZ0pfoYdzWYVjQbRWYFlnVp9AoXN5zA279mmY+yQGNurjgzZNgJMRoXnfGBIZ3z0MwvNZkfAqJUvz8ytUqhX4Le6COW3UAwLAVfRHg8wqhLz4qjVzzIg4GxhLMPDAO+5cc11golRdxOL3pEkauGwgDIwnK1ymNQN/Mld15EQ/bMjboPM4dJzdcRFqqTG1CxYt4HFt9VmVSxVj6KIzXSV+kJqXCsaoDXHs3UqyVSkpIxvTWi+B/K0CxN2V4cCSe+7zAiXXnsdxzDiyKmqNxVxc07uqi9mf4wPMJZnf4I9OLDUnxydg66W/IZXJ0n9JR7fWqCIIAP4/HuLjTE+HBn1C4mCVa/NYY9drWgkicvZ0B3gWEaKw1xhhD6KuCXfm+IKORqjz0vYxUkYIrKiwaPeyHaTUdJBLzaNm3Cf43ug1MC5uiiK3mauXZwRjDPwuOYO/CIxAEBpGIh/zfjZa7T/0f+s77FRzHgTGGg3+cwN/zDiEtVQaRSAS5TA49fT38Nq+byg+nPfMP45/5R9ROU4MDipWyxt8v12cr3rNbL+PgshOKtyNNLY3Rbrgbes3snKP6WNq4d+kRZndYqlV9qLK1SmGj7x9Kx+RyORZ0W4Xbx+8qfp45wfEcwIAZ+8eicbdfAKRX3D+2+hxObryA2Eipoq2ppTHch7TA+Z2eiI2QqusSAFC6mgMadnHBrtkH/ovvixBFYhGa9qiP4av6waywKaa7L4bvefVlHwBATyLGuSTlsiDR4TGY0XYJXvq9USQhglyAnoEeJu8aicZdXbB62Bac3+GpdvStdstqWHR2Ohhj8L8VgFvHfJAUn4wSFezQ4rfGMC+S/nfy77Un49XDt2rfFtU30MOhj9u03lw6NSUN87uuhM8ZP/BiHsIXG1VXbuCERWenw8hU/durUWHRuHvuAZITUuBQqTiOrDoD3wsPNL7NamRmiNLVSyI+OgElnOzgPrQFqjetnK0RtuBn7/H5QzQsbSxQspL9T18UVdvPb0qq8hAlVUQXlvZZi6sHbmeZWPEiHtuerNL41lZOHVl1Glsm/q32/OA/eqPbpA5Zthu3dRjaDGqudGx+t5W4ddQnywTifMp+iPWyN9guCALC30ZCLpPDumRRracQv8Wx1WexafyuLNvpG+jhbKLymqLTmy5i7cjtOX4pQQmXPhp58MNWSAzTK7p/fBOOMfVnIPZTnNLvU/qaIwZBrvnGNo5Wakt4cDyHKg0rYOXVeQCAmMhY9Cs3Osu3PEViHk51y0L6OQ7Fy9ui9cDm2D3nIN76v8s8QvNvmY8Fp6di7v+WQ5ZFEdYNvkuxefxuPLn5/L/kTBAgEvEYs2kIKjdwQn+nMRr7AICJf/2uelRRhU3jduH4unMqkyBexKNxNxdM3zs207m01DRsGrcLZ7ddhiATwPEcmMBgXtRMKQlWh+PSd8vIGG10698U47cNy3I24vGNZ1g3cjve+v+3qbpjlRIYtqofajbX/Hbpj0zbz2+a6yGkgBm9cTCc6pYBAJX/euRFHHgRj8m7RuZKQpWanIq9C49qbLN30VFIo+KwZ/5hje12ztwPWZryB6GBkQS8SPO/ikViPkf1sHieR7FS1ihezjZPEioAcB/qChPLrEc1VJUyOaZmQXKOsPSyFVf33wZjDM99XmJco1mIjojNlKALcgEsi8FQjuPSEyQ1f1RMYHh8/RleP3qbXhy0/R9abVwslwl45h2I94Ef4HP2PmZ3+AOvH75VPeXF0uP4Z97hLBMqAFjaey2eegX+ex855DI5mMAgS5Nj5aBN8D59L8s+AGD1sK2Y879leHRdc7HQhNgEnNlySe2okiAXcO2gFyJVlEJZMWAjzmzxgPDvc2f0Efc5HhyHLP8byfg3ScbP7eLOqzi2+qzGa7xO+WJC0zlKCRUABPm/w1S3BfDVUFyWpKOkipACxsjUECuvzcP0fWNRo3ll2JSyQpHihWBpYw67sjZoO7Qltj5eiea9GubK/R9de5rltjCJ0iQcXXUGiVLNH6IxEbHwvxWgdMylfR2Na0ZEYh6/dKhbYNb/SQwlWda5Eol5NOxcT+lYWmoaQgI/6GaU6gurhmxG56IDMNplOj5/iFbbv6aRQo7noKcvTq9SriE+XsTjzmk/PLn5HM99XmpdgDXj1tpMcwtyAQG+r7Tq933gB7V98jyHqwdua9WPLFWGO2f9MLHpXLVv4gHAM+8XSE3WXN4g4429LwX5v4Pnvlsqf16CIIDj+fQ3avFvcqXlzNzRP88oyloo7s8YHng+wfrROzCv03LVf54sPc7Vw7eqn5YnAGihOiEFklhPjKbd6+fL5tfaFumM/ZT1FAWATAnaL+1rw97JDh9efcyUXKWv2wG6TWqvXbA5EB4ciaAn76AnEaNSfSetNj/OSovfGmP/kuP4FBqV6UOd49NHFjuPVV54z4t4xZSPLjGBZblly9dxAP8lOBzPQd9AHyPXDcDKgZs0XsvxHFKTU+F96h5EYpHGelzfhKXXGtNmtEodQWB46fcGFeqVRaDv6ywTuowRpI1jd6Jq44ooXa1k5jZalj75+vfcc+9NjS8JCHIBidIkLLsyB8fXnIX3Ke1G2D6FRuHj63AUL2cLIH3rppltl+DVgyCtftcigj/B/1YALKzM4X/zOcBxqNqogqI/bTDGEHD3FSLff4J5UTNUbuCk8q3XgoqSKkJItmj7F2i5WqVxFpezbFeslLXS9yKxCH9cmoVprRch+On7f9e+pK/v0TPQw9Q9o+FUt2xOQtcoMuQzVg/firvn7iv+tW5oaoAu49qh16zO3/QXv8RQguVX5mBaq4X48Dpc8UxyuQADYwPMOTIBDhWVN7gWiUSo1aIa7l9+nOO6ZLowftswfHwdjsc3n4HnedR0rYrWg5rD0MQAG0b/pbHiuzwtvVTLA09/rUdTcqKwrSWc29bCBTUL1bOj18wumNdlBRhjWiW0IjGP0xsvYvjqfkhLkcHY3EgxLV+mZqlMZUFUqVBP+fc59lNclq/6ymUCSlYsjoh3n7KVfGeUNJHL5JjScgFCAkMBaL+N09oR2xD8NETpWG23apjy9yjF7grq3L/8GGtHbEfoy4+KY0XsCmHI8t/y5R+IuYGSKkJItpSp4YjS1Usi6Mk71W9a8RxKVCyOVgOb4eCyE/gYFKF6kS7PwbGqg8p/4RctXhhbHi7HvYuP4HPGD6nJaShdvSRa/NY4y+1GciI6Ihajf5mB6DDl6bCkuGTsWXAYnz5EYfzWYdnu99XDIPiefwhZqgzl6pTGNv8/ce/CQ/heeABZmhzl65RB814NMlWmz+DWvynuXXyYw6f6dhJDfTTsXC/T22mMMTy5+Rylqjrg+Z2XKqcKeZ6DWREzuLSvjdhPcbk2SsXxHDqMaI2Oo1vj3bOQTNPJ2WFsboRaLapi1bV5+OO3dQh58THLa+QyAVf23cTZben/gChsVwgdR7RCp3FtUbiYJRp2roebR++o/G9FJOZRuYETHCoUVzpu7VA0yyRHYqgPQzNDvH74VuvnMy9iCtvS6f+I8Tp1D8FP32dxRWbvn4dmOnb/yhNMbDoX6+8uVTuye//KE0xrvSjTc30KjcLinqshl8nh2rtRtuP53lBSRQjJtvHbhmF8o9lIS5VlemtMrC/GhO3DwfM8xm8bjiktF4BBUCr6yYt4iPREGLt5iNp7iEQiOLepCec2NXP1WQDgyMrTiPoYrXpEgQHnt19BhxGtVCaAqsR+kmLhr3/i4VX/9Gm8fwvsWpUogtlHJmLMJvXPneHm0Tv4o8/abyql8C04jkOHEa0yJVQhLz5gbucVCH76Pn2EREVsIjEPXizCzIPjINYTo1nPBtgycTdSElNU1qjKeFMt2zHyHJycy6LT2DaK0cBBlccrjYRoixfxcB/SAmI9MZzqloVT3bL48Dpcq5GvL0frPodG4a+Z++F78SGWXJiJ0RsG4a3/O7x7Hppes+zf5+R5DoVtC2Hy7lGZ+mvRtzF2zzmoPlYxj5b9mkJfoqeoyZUVjuPQYWRrxRuzt47d0WoU7Wuq7iXIBAQ/C4HnvluZ3uYF0pPwjWN3po/+qfmD3jRuFxp3c8mzF0hyS8FY6UkI+a6Uq1Ua6+4sRt02NRRTHRzHoW7rGljrtUgxPVetSSWsvDYPFX8pr3R91cYVsfrmglyZxssuxhjOb7+s8cNFJBbh4s6rWvUnl8kx1W0hHt94BiB9/UvGKM2n0ChMaj4XH4PCNfbx9ul7LOrxJ2QyeY4SKo7nFHWXcqpJ9/oY8NUC++iIWIxvPBvvA9RPGYn1xWjaowE2+v6Bao0rAUh/uWLaP2PAifhMW+RwPIfydcrAoZJ9tt7otLAyR5/ZXbH88mxFiQixnhj6Btn/UOZFPEpWskevmZ0Vx8KDI3M8lciE9JG8E2vPwaywKdbdWYyhK36DfTlbGJoYoFgpa/Sd3x2b7i9T2kkgg5V9EfSepXqLKF7Ew6KIGXrO6KSYjtXm51anTQ30mPY/xffJCSk6nVbmeA6Xdv3338iXC9pfP3qL4KfvNY6+ST/H4d7F9P0fn3kHYknvNejt+Dv6lhuFjWN3IiQHiXJ+oJEqQkiOOFZxwIKTUyH9HIfo8BhYWJmr/CCv9Et5/HljAcKDIxEdHoPCtoWUKqjnN1maDHHRmt9mFOQCPoVmfu1dlTtn/PDqQZDaflKTUnHsz7MYsXaA2j5OZLxRpmU+xYvSS0zIUmWwciiCDr+3QoPOzuhXbnS2F7oXL1cMU/4ehfJ1ymQq2XFm06VMNa2+Nm7zULTs1yTT8V861MGaWwuxb/Ex3DntB8YYDIwlaPJrffy+pj+iw2IwvvFsRIXFaBWzY5USeHzjKbxP3UOJCnZwH9IClRs4oWQle7x7rr7qOMdzMLEwRlx0+puLpoVM0G5YS/w6paPSqFyhYhY5GsnJwASGkxsuoOvE9jA0MUTncW3ReVxbra/vM6crLKzM8c/CI4gOi0mPnePg7F4TI9cOUBTz7TqpA+5dUr8ZtVhPhLFbh8G1d0OldYElnOxw54yfzhIrJjB8Co3C5vG7cHHXNcTHJMC8iClaD3JFqaoltOrjU2gUDvxxAjum7VVaqH9y4wWc3nQRc45OQr22tXQSb26h4p95iIp/EvL9YYyhg/lvSIpXv2GuSMyjzeAWGL1hUJb9Leq5GjcOe2v8sDKxNMbxz7vUnu9efEh6uYMsZEybjd08BG0Gu0KQC0rbnqwevhXntl7WerTLtrQ11ngtUrvg+LcyI/HxjfpRNo7nULN5FSy9OEvlef/bAZjbaTliI6XgxTzA/t28uXpJLDg1FRIjfZzf7onDK06mL9bWUsYHsPsQVzT5tT4mNZ+nsX2P6Z3Q4H91YVWiCEwLmah8CcHnrB9mtluqdQzqnI7/55veIJXL5Ai89xopiSmwL2+LInaZ/0FyauNFrB+1AxzPQZAL6b8XACyKmmPZ5dlwrJw5qQl+9h6DKo/PcVxfy3iLlQks05IA86JmisRQE9NCJmrfTOU4QKyvhz1vNqBwMUulc3KZHF6n7uHagVuQRsXDrkwxtBncHOVqlf6mZ/oSFf8khBAtcByHln2bpH/IqyGXCWjxW2Ot+ouLis/yX/9JWRTB1FSn60t2ZYth5sHxcB/SAhzHZdpHbsSa/oq4eV7z22SNurpg3Z0lGt/gio/WXIqBCUxtMhT66iOmtlwA6ef084JMUPyc3vq/w2TXeTAwNsCvkztkehMyKxk/r7NbL+P1o7do/7sbANXFcQFg/+JjGFFnKma2XYqXfm9UtqndqjqqNq6oemqNS1/XlNUbjRzHQaz3beUCRGIRKtYrhxrNqqhMqACg/e9u2Bm4Bp3HuqNak0qo3aoGRm8YjN0v16lMqFJT0jCvy4pviutrTGBKf6YZBLmgVUIFQGOpD8YAeZoM57Ypv1Ec+0mKkc7TML/LCtw6fhcPPf1x4a8rGFFnKtaP2pHn6xEpqSKE/PS6Te4AE3NjlR+gHM+hQSdnRRX7rNiVscm0bki5Q8CmpJXGPiq6lNPYB8/zaNazAf56vkbj5sB6+nqYtHMEdgauRe/ZXdGgkzOKfDX1WqqaA5Zdno1ZB8fDrLCpxriKlbZJ30dQDZGYh125YirPHf3zLGRpMpVTe3KZgJAXH3HrmE96P9+QiBxZeRrDV/fDmE1DUKyU5p9zoO8rjGs0G4EqioeKRCIsPD0VTX79JdMzV3Auh8I2lllPz3LIVJ08t9iVKYYhy3/DCs+5WHx2OtoNa6lyT0FBELBt8j94H/BBZ/fO+PnkdgIjCCxTFftFPVbjzePg9PP/JnSKacMNF3B8jQ53JdACJVWEkJ+elX0R/HlzAcrUcFQ6LhKL0HZoC0zbO0brDWXbDHbVONLEcRzaDmupsY//jW6jsQ/G/t/encdFVbZ9AP+dAYZVFtlRVlFxAURRHqz0SVFQLEvzNSXFJQ2XFO1NSRJEI3xe09RUTOvVt7TcHrfMNMQl8SFcApNMQEFRBFGRTWSbud4/iKMj26DDYMP1/Xzmk5z7Pof7mqMzV+dc574J48NHKz2mjp1tMSFiDCL3/De+z96Ir/74HP9zLAJfX16NL5M/g9cg5dZ0G/HekEbrnWTVcgx/16/etpM7zzT+vkgEnN6TCADoN8zrmRfwvZdTgLzMfIx4bwi2pn+Bb66ua3TB7OrKanz+3pf1tukb1RTYf3cjFh9umYX5m0Ow6feVeHXcS/UuLVOftbO/eqY4WsKVsxmY1GUO9n/x7IlGzRI5j1MHHT0deP6zhyqGp5Ti+yViEfy1i9eRHH+p0SvDO1ccqDOLfEviQnXGGENN4e76s8uR8VsmrqVch1RPB72HeDQ5oeHTOnk6YfS8Efj354fqtEm0JOjc27nJpKrXqz0xIWIMvl26W6FYWktbAplMjrmx0+rMbdQcjt06PtP+fhMGIO7bU/gjIa3uciUCMGjcy/Aa1LPefcsfNlyzBtTcPir9a7b+oZP+iW3L9qCs+NEzXf2Q1c7+Lgi4/Gs6KssrG+1/LeU6bl/Lg10nm3rbLTqYY2jwPwHUrH05t3+4UuMgOeHPxHRkX8kR1+HMvpKDE98noOheCawdLeE3YUCdGqGWcDMtBx8Ojmpy2ZymCYg5Go6TO/6DwjtF6NjFFqZWxkg5ntr0riqQ9Xs2okZ/hsW75uP80YtNPkxQkPsA2ZdvwdndUS3j46SKMcae0Lm3Czr3dnmuY7z32UTYdbLBjn/tw92bNVc09Ax1MfxdP0xaNlapwuWJS/4L3Xy7YO+aH/FHwhUIEgF9hnhi9LwR6PHUFBXqoiPVQcxP4di6eCcObYpD+V/F/cbm7TB63giMXTiywStMHbvY4XrqzQaTJC1tCZz+qqUybt8OMUc+Rpj/sibXj3xaOzNDcYJLALjQyJNxT7qSlNFgUvWk72P2NfpQQ31yM+/ArpM1Pp/+JX7+v5OQaNfMXUYyOf43/DtMWvq2wnQHLeH7mH2oqqh67qf9er3aA4sCaibxFLQkSDr8W80xBah8ncqGJB48j2+W7IKuvm5TE88DAKqr+EoVY4z9bQmCgNdn+mNEyBDcTLuN6spqdOhs2+ynwPr690Jf/14tM8hnpKuvi/c+m4jgpWNx4/ItaGlJ4NijY5OTNr4+MwBrZm5qsF1WLcfw6Y9vHXbz6Yzt12MRPiIGl/+TptTYBEnNBJdPjqW6Srn1AKuU6FddVY2DG44qdbwnGZkaInb+VsR9cwrA43UDaxD+N/w7mFi0w/Bp9d86fV4ymQwndjR++/VpWjpakD2RjJhYGqOPnweOf5/wuJP8iWRFjfXgRDXTVYR9O6fJmPQMddGxq/JrEz4vrqlijLEWIpFI4NitIzp5OqlkYeYXiZ6BLrp6d4Krl7NSs2AHTHkVXoPc6xR9117ZCvp4dJ0n1YxMDbH8SDi6+3ap6dtAkXzt9j5+HhgfPkqhzWOAcvU+ylydvHvzvvj0orIsOrSHtZMFDn0Z1+itzG+X7la69qe08CF2LN+H4C7vY6TpREzpHoo9q37Ao9L6r+pVlFU2e7Hpj3fMw/YbsVh+9GN8fnoZvsuORaqSyW2jD2qoSFnxIxibG8HWxbrByU8lWhIETBkEfUO9Fh9PLZ6nSo14nirGWFtWWVGFnf/ajwPrj6DobjEAwKFbB7y98M1Gp6yorKhC/LZf8OOmOORdvwsjU0NY2psjJz0X5Q8r0KGLLV6f4Y9B41+uM63Ew6KHGGU55amrQ4qsHC2wLXNDk8Xxd2/dx3iH5q0BuWDrbFRVVuPz6Rub7LsuKQZd+zb+lOm92wWY98pi3Llx9/FDAwIgQIC9mx1WnVpaZxJeuVyOUeaT8fCvmrXG6OpL8cHXM/Dq2y8rbL+akoUZvRc0uq8gCBg9LxCPSsrxIL8I2X/eQs7VvGZPQKus1QmfQNdAiv8etASPSsof39r86/3o4t0JK+IjGlxbszmU/f7m23+MMcbUQqqrgwkRYzB+0SjcyymAlo4WzG3NmkxmpLo6GDZ1MIZNrbuuXFMMTQwxMfK/sHXxjgb7zFn3rlJPG1p0aA97NzulpiMQJAJmr52KIRMHYu+aH2vWSWwiuVCmVmvFpHW4e/Oe4rEIIBBupedi7czNWLzrA4V9JBIJhr87GP9e/WOjNVUhK4MxKjSw3vfiyfUNGyJIBJhZm+K9z0bWxPOwHJFv/A+S4y/VW3NV++DFkAkD8WdSBm6lKT/Ng66BLlw8HKBvpI9NKZ9h75rDOLbtF5QVl8Ha0QqvhQxF4Ht+4hJG6sJJFWOMMbXS0taCtaOl2n7f+EWjIAgCti3bjaqKavEL3tjcCHNjp8MnULmlTwRBwNsL38SKyeub7BsU/pY4CalDt45NJlSCIKBjA3N81bqVfhu/HbvUYLtcJsfpvUm4l3O/zkShYxe+gV/+/Svu3bpfbx3S22FvNrqMTgdXmyYTQ7lMDsfuj58q1TfUw/KjHyM5/hKObfsFWZeyUZhfhOL7pdDS0YL3EA+MnjcCPV/uhuqqagQ5zURhflGTxfSCREDgND/xCpSVgyVCVgYjZGVwo/upAydVjDHGNJogCBi/aBRGzg5A4sHzKL5XAmsnS/Qb7qVUPdiThkwciCtnr+KH2PoL1gUBMDA2wOuz/MVtvf3cYeVoWfcK018kWhL0G+7V4Izptf5MymhyfCQnpJ/PrHMsEwtjrP1PNGLnb8Uve34Vb4ea2ZjWvDezAho9rpm1KV56ox/+c+BcvUmPIBHQ3sYU3gG9FGOTSNBniCf6DPFs9PjaOtpYdnAhFvgtxaPS8gZ/B8kJ7q90w5RPxzV6vNbCSRVjjLE2wdDYAH7vDHiuYwiCgDnr34WzhwPWv//146s+f139MjQ1xKeHw2Fm9Xh+M4lEgrBv3sfCoUshe2opF4m2BMbt22HWmoYX2K6l1UBBdp1+DRSKt7cxQ/h38zBzdRFupd2GVF8K115OderQGjLj80m4nJiOwrtFCjVqtQt6h307p951FJXVpU8nbL60CgfXH8GJHWdQVvwIBqYGkFXJIKuqhq2LNQKnD8Gg8S9DW+fFTF+4UF2NuFCdMcY0R3FBCX7eehKpZ65AIhHgNcgdg98ZUO/yMEBNsfe2pXuQePAc5HKCjp4OhrwzAO9EjIFlx8avUgE1RerjHUIavQWno6uNnbc3o52Z0TPH1dQYti3dg7hvTqKyvAqCRMA/RvTBO4vfUukCxi8aZb+/OalSI06qGGOMlZU8QmnhQ5hYtGt2IfXyCWtxYseZBm+PvT7DH7O/mKqqoTaosrwShXeLYWhiAENjgxb/fa1N2e9vnqeqmdavXw8nJyfo6enBx8cHZ8+ebe0hMcYY+xsxaKcPK3uLZ3oybW7sNHFG/dr5mWr/6+3fC9NXTFDdQBsh1ZPCyt6iTSRUzfFi3pR8Qe3cuRPz58/Hxo0b4ePjg9WrV8Pf3x9paWmwsmp8NXTGGGPseekb6WNFfCSSfvwNP//fSdy7XQArBwsETB4Eb39PSCR8raQ18e2/ZvDx8UHfvn2xbt06ADUTqtnb2+P9999HWFhYk/vz7T/GGGPs74dv/6lYZWUlLly4AD+/x2szSSQS+Pn5ITExsd59KioqUFxcrPBijDHGmGbipEpJ9+7dg0wmg7W1tcJ2a2tr5OXl1btPTEwMTExMxJe9vb06hsoYY4yxVsBJVQv66KOPUFRUJL5u3rzZ2kNijDHGWAvhQnUlWVhYQEtLC3fu3FHYfufOHdjY2NS7j66uLnR1NWtlesYYY4zVj69UKUkqlaJPnz6Ij48Xt8nlcsTHx8PX17cVR8YYY4yxFwFfqWqG+fPnIzg4GN7e3ujXrx9Wr16Nhw8fYvLkya09NMYYY4y1Mk6qmmHs2LG4e/cuIiIikJeXh169euHIkSN1itcZY4wx1vbwPFVqxPNUMcYYY38/PE8VY4wxxpgacVLFGGOMMaYCXFOlRrV3WnlmdcYYY+zvo/Z7u6mKKU6q1KikpAQAeGZ1xhhj7G+opKQEJiYmDbZzoboayeVy3L59G+3atYMgCK09HKUUFxfD3t4eN2/ebHPF9W01do67bcUNtN3YOW6OW1lEhJKSEtjZ2UEiabhyiq9UqZFEIkHHjh1bexjPxNjYuE3943tSW42d42572mrsHHfb8qxxN3aFqhYXqjPGGGOMqQAnVYwxxhhjKsBJFWuUrq4uIiMj2+TC0G01do67bcUNtN3YOW6OW9W4UJ0xxhhjTAX4ShVjjDHGmApwUsUYY4wxpgKcVDHGGGOMqQAnVYwxxhhjKsBJVRv1yy+/4LXXXoOdnR0EQcD+/fsV2okIERERsLW1hb6+Pvz8/JCRkaHQp6CgAEFBQTA2NoapqSmmTp2K0tJSNUbRfDExMejbty/atWsHKysrvPHGG0hLS1PoU15ejlmzZsHc3BxGRkYYPXo07ty5o9AnOzsbgYGBMDAwgJWVFT788ENUV1erM5RmiY2NhYeHhzjpna+vL3766SexXRNjrs/y5cshCAJCQ0PFbZoa+5IlSyAIgsLLzc1NbNfUuAEgJycH77zzDszNzaGvrw93d3ecP39ebNfEzzcnJ6c651sQBMyaNQuA5p5vmUyGxYsXw9nZGfr6+ujUqROWLVumsEafWs83sTbp8OHDFB4eTnv37iUAtG/fPoX25cuXk4mJCe3fv58uXrxIr7/+Ojk7O9OjR4/EPgEBAeTp6Um//vornT59mlxdXWncuHFqjqR5/P39acuWLZSamkopKSk0fPhwcnBwoNLSUrFPSEgI2dvbU3x8PJ0/f57+8Y9/UP/+/cX26upq6tmzJ/n5+VFycjIdPnyYLCws6KOPPmqNkJRy8OBB+vHHHyk9PZ3S0tJo0aJFpKOjQ6mpqUSkmTE/7ezZs+Tk5EQeHh40d+5ccbumxh4ZGUk9evSg3Nxc8XX37l2xXVPjLigoIEdHR5o0aRIlJSVRZmYmHT16lK5evSr20cTPt/z8fIVzHRcXRwDoxIkTRKS55zs6OprMzc3p0KFDlJWVRbt37yYjIyNas2aN2Eed55uTKlYnqZLL5WRjY0MrVqwQtxUWFpKuri59//33RER0+fJlAkDnzp0T+/z0008kCALl5OSobezPKz8/nwDQqVOniKgmTh0dHdq9e7fY588//yQAlJiYSEQ1CalEIqG8vDyxT2xsLBkbG1NFRYV6A3gOZmZm9NVXX7WJmEtKSqhz584UFxdHAwcOFJMqTY49MjKSPD09623T5LgXLlxIL7/8coPtbeXzbe7cudSpUyeSy+Uafb4DAwNpypQpCttGjRpFQUFBRKT+8823/1gdWVlZyMvLg5+fn7jNxMQEPj4+SExMBAAkJibC1NQU3t7eYh8/Pz9IJBIkJSWpfczPqqioCADQvn17AMCFCxdQVVWlELubmxscHBwUYnd3d4e1tbXYx9/fH8XFxfjjjz/UOPpnI5PJsGPHDjx8+BC+vr5tIuZZs2YhMDBQIUZA8893RkYG7Ozs4OLigqCgIGRnZwPQ7LgPHjwIb29vjBkzBlZWVvDy8sLmzZvF9rbw+VZZWYlt27ZhypQpEARBo893//79ER8fj/T0dADAxYsXkZCQgGHDhgFQ//nmBZVZHXl5eQCg8I+r9ufatry8PFhZWSm0a2tro3379mKfF51cLkdoaCheeukl9OzZE0BNXFKpFKampgp9n469vvemtu1FdenSJfj6+qK8vBxGRkbYt28funfvjpSUFI2NGQB27NiB3377DefOnavTpsnn28fHB1u3bkXXrl2Rm5uLqKgovPLKK0hNTdXouDMzMxEbG4v58+dj0aJFOHfuHObMmQOpVIrg4OA28fm2f/9+FBYWYtKkSQA0++95WFgYiouL4ebmBi0tLchkMkRHRyMoKAiA+r/POKlibdasWbOQmpqKhISE1h6KWnTt2hUpKSkoKirCnj17EBwcjFOnTrX2sFrUzZs3MXfuXMTFxUFPT6+1h6NWtf+nDgAeHh7w8fGBo6Mjdu3aBX19/VYcWcuSy+Xw9vbGp59+CgDw8vJCamoqNm7ciODg4FYenXp8/fXXGDZsGOzs7Fp7KC1u165d2L59O7777jv06NEDKSkpCA0NhZ2dXaucb779x+qwsbEBgDpPhty5c0dss7GxQX5+vkJ7dXU1CgoKxD4vstmzZ+PQoUM4ceIEOnbsKG63sbFBZWUlCgsLFfo/HXt9701t24tKKpXC1dUVffr0QUxMDDw9PbFmzRqNjvnChQvIz89H7969oa2tDW1tbZw6dQpr166FtrY2rK2tNTb2p5mamqJLly64evWqRp9zW1tbdO/eXWFbt27dxFufmv75duPGDRw7dgzvvvuuuE2Tz/eHH36IsLAwvP3223B3d8eECRMwb948xMTEAFD/+eakitXh7OwMGxsbxMfHi9uKi4uRlJQEX19fAICvry8KCwtx4cIFsc/x48chl8vh4+Oj9jEri4gwe/Zs7Nu3D8ePH4ezs7NCe58+faCjo6MQe1paGrKzsxViv3TpksI/wri4OBgbG9f5MH+RyeVyVFRUaHTMgwcPxqVLl5CSkiK+vL29ERQUJP5ZU2N/WmlpKa5duwZbW1uNPucvvfRSnWlS0tPT4ejoCECzP98AYMuWLbCyskJgYKC4TZPPd1lZGSQSxVRGS0sLcrkcQCuc72csuGd/cyUlJZScnEzJyckEgFatWkXJycl048YNIqp5BNXU1JQOHDhAv//+O40cObLeR1C9vLwoKSmJEhISqHPnzi/0I8dERDNmzCATExM6efKkwuPHZWVlYp+QkBBycHCg48eP0/nz58nX15d8fX3F9tpHj4cOHUopKSl05MgRsrS0fKEfPQ4LC6NTp05RVlYW/f777xQWFkaCINDPP/9MRJoZc0OefPqPSHNj/+CDD+jkyZOUlZVFZ86cIT8/P7KwsKD8/Hwi0ty4z549S9ra2hQdHU0ZGRm0fft2MjAwoG3btol9NPXzTSaTkYODAy1cuLBOm6ae7+DgYOrQoYM4pcLevXvJwsKCFixYIPZR5/nmpKqNOnHiBAGo8woODiaimsdQFy9eTNbW1qSrq0uDBw+mtLQ0hWPcv3+fxo0bR0ZGRmRsbEyTJ0+mkpKSVohGefXFDIC2bNki9nn06BHNnDmTzMzMyMDAgN58803Kzc1VOM7169dp2LBhpK+vTxYWFvTBBx9QVVWVmqNR3pQpU8jR0ZGkUilZWlrS4MGDxYSKSDNjbsjTSZWmxj527FiytbUlqVRKHTp0oLFjxyrM1aSpcRMR/fDDD9SzZ0/S1dUlNzc32rRpk0K7pn6+HT16lADUiYVIc893cXExzZ07lxwcHEhPT49cXFwoPDxcYRoIdZ5vgeiJaUcZY4wxxtgz4ZoqxhhjjDEV4KSKMcYYY0wFOKlijDHGGFMBTqoYY4wxxlSAkyrGGGOMMRXgpIoxxhhjTAU4qWKMMcYYUwFOqhhjjDHGVICTKsaYRpo0aRIEQajzunr16nMfe+vWrTA1NX3+QTZDdHQ0+vfvDwMDA7X/bsaYcjipYoxprICAAOTm5iq8nl5Eu7VVVVUp1a+yshJjxozBjBkzWnhEjLFnxUkVY0xj6erqwsbGRuGlpaWFAwcOoHfv3tDT04OLiwuioqJQXV0t7rdq1Sq4u7vD0NAQ9vb2mDlzJkpLSwEAJ0+exOTJk1FUVCRe/VqyZAkAQBAE7N+/X2EMpqam2Lp1KwDg+vXrEAQBO3fuxMCBA6Gnp4ft27cDAL766it069YNenp6cHNzw4YNGxSOExUVhXnz5sHd3b1l3izG2HPTbu0BMMaYOp0+fRoTJ07E2rVr8corr+DatWuYPn06ACAyMhIAIJFIsHbtWjg7OyMzMxMzZ87EggULsGHDBvTv3x+rV69GREQE0tLSAABGRkbNGkNYWBhWrlwJLy8vMbGKiIjAunXr4OXlheTkZEybNg2GhoYIDg5W7RvAGGsxnFQxxjTWoUOHFBKeYcOG4cGDBwgLCxOTFRcXFyxbtgwLFiwQk6rQ0FBxHycnJ3zyyScICQnBhg0bIJVKYWJiAkEQYGNj80zjCg0NxahRo8SfIyMjsXLlSnGbs7MzLl++jC+//JKTKsb+RjipYoxprFdffRWxsbHiz4aGhvDw8MCZM2cQHR0tbpfJZCgvL0dZWRkMDAxw7NgxxMTE4MqVKyguLkZ1dbVC+/Py9vYW//zw4UNcu3YNU6dOxbRp08Tt1dXVMDExee7fxRhTH06qGGMay9DQEK6urgrbSktLERUVpXClqJaenh6uX7+OESNGYMaMGYiOjkb79u2RkJCAqVOnorKystGkShAEEJHCtvoK0Q0NDRXGAwCbN2+Gj4+PQj8tLa2mg2SMvTA4qWKMtSm9e/dGWlpanWSr1oULFyCXy7Fy5UpIJDXP8uzatUuhj1QqhUwmq7OvpaUlcnNzxZ8zMjJQVlbW6Hisra1hZ2eHzMxMBAUFNTccxtgLhJMqxlibEhERgREjRsDBwQFvvfUWJBIJLl68iNTUVHzyySdwdXVFVVUVvvjiC7z22ms4c+YMNm7cqHAMJycnlJaWIj4+Hp6enjAwMICBgQEGDRqEdevWwdfXFzKZDAsXLoSOjk6TY4qKisKcOXNgYmKCgIAAVFRU4Pz583jw4AHmz58PAMjOzkZBQQGys7Mhk8mQkpICAHB1dW12oTxjrIUQY4xpoODgYBo5cmS9bUeOHKH+/fuTvr4+GRsbU79+/WjTpk1i+6pVq8jW1pb09fXJ39+fvvnmGwJADx48EPuEhISQubk5AaDIyEgiIsrJyaGhQ4eSoaEhde7cmQ4fPkwmJia0ZcsWIiLKysoiAJScnFxnTNu3b6devXqRVColMzMzGjBgAO3du1chHgB1XidOnHjOd4oxpioC0VMFAIwxxhhjrNl48k/GGGOMMRXgpIoxxhhjTAU4qWKMMcYYUwFOqhhjjDHGVICTKsYYY4wxFeCkijHGGGNMBTipYowxxhhTAU6qGGOMMcZUgJMqxhhjjDEV4KSKMcYYY0wFOKlijDHGGFMBTqoYY4wxxlTg/wGb4j1v7JjLJwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用DBSCAN聚类\n",
    "dbscan = DBSCAN(eps=0.5, min_samples=5)  # eps和min_samples需要根据实际情况调整\n",
    "clusters = dbscan.fit_predict(scaled_data)\n",
    "df['cluster']=clusters\n",
    "# 打印每个簇的数量\n",
    "print(df['cluster'].value_counts())\n",
    "\n",
    "# 绘制聚类结果\n",
    "plt.scatter(df['主沟长度'], df['周长'], c=df['cluster'], cmap='viridis')\n",
    "plt.xlabel('Feature1')\n",
    "plt.ylabel('Feature2')\n",
    "plt.title('DBSCAN Clustering')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv2",
   "language": "python",
   "name": "python3"
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